From 5285d6422c75538c6bceea4d0eb9a3a858e45ecf Mon Sep 17 00:00:00 2001 From: Michel Lang Date: Thu, 18 Nov 2021 11:43:25 +0100 Subject: [PATCH 1/4] ... --- R/LearnerClassifCVGlmnet.R | 14 +++++++++- R/LearnerClassifGlmnet.R | 14 +++++++++- R/LearnerClassifLogReg.R | 11 +++++++- R/LearnerRegrCVGlmnet.R | 14 +++++++++- R/LearnerRegrGlmnet.R | 14 +++++++++- R/LearnerRegrLM.R | 11 +++++++- R/LearnerSurvCVGlmnet.R | 14 +++++++++- R/LearnerSurvGlmnet.R | 14 +++++++++- R/helpers.R | 5 ++-- R/helpers_glmnet.R | 34 +++++++++++++++++++++++++ R/helpers_lin_models.R | 20 +++++++++++++++ man/mlr_learners_classif.cv_glmnet.Rd | 20 ++++++++++++++- man/mlr_learners_classif.glmnet.Rd | 20 ++++++++++++++- man/mlr_learners_classif.kknn.Rd | 2 +- man/mlr_learners_classif.lda.Rd | 2 +- man/mlr_learners_classif.log_reg.Rd | 18 ++++++++++++- man/mlr_learners_classif.multinom.Rd | 2 +- man/mlr_learners_classif.naive_bayes.Rd | 2 +- man/mlr_learners_classif.nnet.Rd | 2 +- man/mlr_learners_classif.qda.Rd | 2 +- man/mlr_learners_classif.ranger.Rd | 2 +- man/mlr_learners_classif.svm.Rd | 2 +- man/mlr_learners_classif.xgboost.Rd | 2 +- man/mlr_learners_regr.cv_glmnet.Rd | 20 ++++++++++++++- man/mlr_learners_regr.glmnet.Rd | 20 ++++++++++++++- man/mlr_learners_regr.kknn.Rd | 2 +- man/mlr_learners_regr.km.Rd | 2 +- man/mlr_learners_regr.lm.Rd | 18 ++++++++++++- man/mlr_learners_regr.ranger.Rd | 2 +- man/mlr_learners_regr.svm.Rd | 2 +- man/mlr_learners_regr.xgboost.Rd | 2 +- man/mlr_learners_surv.cv_glmnet.Rd | 20 ++++++++++++++- man/mlr_learners_surv.glmnet.Rd | 20 ++++++++++++++- man/mlr_learners_surv.ranger.Rd | 2 +- man/mlr_learners_surv.xgboost.Rd | 2 +- tests/testthat/test_classif_cv_glmnet.R | 4 +++ tests/testthat/test_regr_glmnet.R | 8 +++++- tests/testthat/test_surv_glmnet.R | 2 +- 38 files changed, 330 insertions(+), 37 deletions(-) create mode 100644 R/helpers_lin_models.R diff --git a/R/LearnerClassifCVGlmnet.R b/R/LearnerClassifCVGlmnet.R index ec03ca04..dc78d0da 100644 --- a/R/LearnerClassifCVGlmnet.R +++ b/R/LearnerClassifCVGlmnet.R @@ -79,7 +79,7 @@ LearnerClassifCVGlmnet = R6Class("LearnerClassifCVGlmnet", param_set = ps, predict_types = c("response", "prob"), feature_types = c("logical", "integer", "numeric"), - properties = c("weights", "twoclass", "multiclass", "selected_features"), + properties = c("importance", "selected_features", "weights", "twoclass", "multiclass"), packages = c("mlr3learners", "glmnet"), man = "mlr3learners::mlr_learners_classif.cv_glmnet" ) @@ -95,6 +95,18 @@ LearnerClassifCVGlmnet = R6Class("LearnerClassifCVGlmnet", #' @return (`character()`) of feature names. selected_features = function(lambda = NULL) { glmnet_selected_features(self, lambda) + }, + + #' @description + #' Returns importance scores, calculated from the path of lambda values. + #' First, the largest `lambda` at which the feature was first included in the model + #' with a nonzero coefficient is determined. + #' Second, the [rank()] of these lambda values is calculated (using averaging for ties) + #' and returned as importance scores. + #' + #' @return (named `numeric()`) of importance scores. + importance = function() { + glmnet_importance(self) } ), diff --git a/R/LearnerClassifGlmnet.R b/R/LearnerClassifGlmnet.R index ee117ae7..2e652325 100644 --- a/R/LearnerClassifGlmnet.R +++ b/R/LearnerClassifGlmnet.R @@ -89,7 +89,7 @@ LearnerClassifGlmnet = R6Class("LearnerClassifGlmnet", param_set = ps, predict_types = c("response", "prob"), feature_types = c("logical", "integer", "numeric"), - properties = c("weights", "twoclass", "multiclass"), + properties = c("selected_features", "importance", "weights", "twoclass", "multiclass"), packages = c("mlr3learners", "glmnet"), man = "mlr3learners::mlr_learners_classif.glmnet" ) @@ -105,6 +105,18 @@ LearnerClassifGlmnet = R6Class("LearnerClassifGlmnet", #' @return (`character()`) of feature names. selected_features = function(lambda = NULL) { glmnet_selected_features(self, lambda) + }, + + #' @description + #' Returns importance scores, calculated from the path of lambda values. + #' First, the largest `lambda` at which the feature was first included in the model + #' with a nonzero coefficient is determined. + #' Second, the [rank()] of these lambda values is calculated (using averaging for ties) + #' and returned as importance scores. + #' + #' @return (named `numeric()`) of importance scores. + importance = function() { + glmnet_importance(self) } ), diff --git a/R/LearnerClassifLogReg.R b/R/LearnerClassifLogReg.R index ba3a0759..f456d764 100644 --- a/R/LearnerClassifLogReg.R +++ b/R/LearnerClassifLogReg.R @@ -53,7 +53,7 @@ LearnerClassifLogReg = R6Class("LearnerClassifLogReg", param_set = ps, predict_types = c("response", "prob"), feature_types = c("logical", "integer", "numeric", "character", "factor", "ordered"), - properties = c("weights", "twoclass", "loglik"), + properties = c("weights", "twoclass", "loglik", "importance"), packages = c("mlr3learners", "stats"), man = "mlr3learners::mlr_learners_classif.log_reg" ) @@ -63,6 +63,15 @@ LearnerClassifLogReg = R6Class("LearnerClassifLogReg", #' Extract the log-likelihood (e.g., via [stats::logLik()] from the fitted model. loglik = function() { extract_loglik(self) + }, + + #' @description + #' Importance scores as \eqn{-log_{10}()}{-log10()} transformed \eqn{p}-values, + #' extracted from [summary()]. + #' Does not work if the model has been fitted on factor features with more than 2 levels. + #' @return Named `numeric()`. + importance = function() { + lin_model_importance(self) } ), diff --git a/R/LearnerRegrCVGlmnet.R b/R/LearnerRegrCVGlmnet.R index c2938587..8059df37 100644 --- a/R/LearnerRegrCVGlmnet.R +++ b/R/LearnerRegrCVGlmnet.R @@ -79,7 +79,7 @@ LearnerRegrCVGlmnet = R6Class("LearnerRegrCVGlmnet", id = "regr.cv_glmnet", param_set = ps, feature_types = c("logical", "integer", "numeric"), - properties = c("weights", "selected_features"), + properties = c("importance", "selected_features", "weights"), packages = c("mlr3learners", "glmnet"), man = "mlr3learners::mlr_learners_regr.cv_glmnet" ) @@ -95,6 +95,18 @@ LearnerRegrCVGlmnet = R6Class("LearnerRegrCVGlmnet", #' @return (`character()`) of feature names. selected_features = function(lambda = NULL) { glmnet_selected_features(self, lambda) + }, + + #' @description + #' Returns importance scores, calculated from the path of lambda values. + #' First, the largest `lambda` at which the feature was first included in the model + #' with a nonzero coefficient is determined. + #' Second, the [rank()] of these lambda values is calculated (using averaging for ties) + #' and returned as importance scores. + #' + #' @return (named `numeric()`) of importance scores. + importance = function() { + glmnet_importance(self) } ), diff --git a/R/LearnerRegrGlmnet.R b/R/LearnerRegrGlmnet.R index a19b7cf4..3f8d0531 100644 --- a/R/LearnerRegrGlmnet.R +++ b/R/LearnerRegrGlmnet.R @@ -79,7 +79,7 @@ LearnerRegrGlmnet = R6Class("LearnerRegrGlmnet", id = "regr.glmnet", param_set = ps, feature_types = c("logical", "integer", "numeric"), - properties = "weights", + properties = c("weights", "selected_features", "importance"), packages = c("mlr3learners", "glmnet"), man = "mlr3learners::mlr_learners_regr.glmnet" ) @@ -95,6 +95,18 @@ LearnerRegrGlmnet = R6Class("LearnerRegrGlmnet", #' @return (`character()`) of feature names. selected_features = function(lambda = NULL) { glmnet_selected_features(self, lambda) + }, + + #' @description + #' Returns importance scores, calculated from the path of lambda values. + #' First, the largest `lambda` at which the feature was first included in the model + #' with a nonzero coefficient is determined. + #' Second, the [rank()] of these lambda values is calculated (using averaging for ties) + #' and returned as importance scores. + #' + #' @return (named `numeric()`) of importance scores. + importance = function() { + glmnet_importance(self) } ), diff --git a/R/LearnerRegrLM.R b/R/LearnerRegrLM.R index 755843ad..333ceaa2 100644 --- a/R/LearnerRegrLM.R +++ b/R/LearnerRegrLM.R @@ -41,7 +41,7 @@ LearnerRegrLM = R6Class("LearnerRegrLM", param_set = ps, predict_types = c("response", "se"), feature_types = c("logical", "integer", "numeric", "factor", "character"), - properties = c("weights", "loglik"), + properties = c("weights", "loglik", "importance"), packages = c("mlr3learners", "stats"), man = "mlr3learners::mlr_learners_regr.lm" ) @@ -51,6 +51,15 @@ LearnerRegrLM = R6Class("LearnerRegrLM", #' Extract the log-likelihood (e.g., via [stats::logLik()] from the fitted model. loglik = function() { extract_loglik(self) + }, + + #' @description + #' Importance scores as \eqn{-log_{10}()}{-log10()} transformed \eqn{p}-values, + #' extracted from [summary()]. + #' Does not work if the model has been fitted on factor features with more than 2 levels. + #' @return Named `numeric()`. + importance = function() { + lin_model_importance(self) } ), diff --git a/R/LearnerSurvCVGlmnet.R b/R/LearnerSurvCVGlmnet.R index 2f67547c..f7ce361c 100644 --- a/R/LearnerSurvCVGlmnet.R +++ b/R/LearnerSurvCVGlmnet.R @@ -75,7 +75,7 @@ LearnerSurvCVGlmnet = R6Class("LearnerSurvCVGlmnet", param_set = ps, feature_types = c("logical", "integer", "numeric"), predict_types = c("crank", "lp"), - properties = c("weights", "selected_features"), + properties = c("importance", "selected_features", "weights"), packages = c("mlr3learners", "glmnet"), man = "mlr3learners::mlr_learners_surv.cv_glmnet" ) @@ -91,6 +91,18 @@ LearnerSurvCVGlmnet = R6Class("LearnerSurvCVGlmnet", #' @return (`character()`) of feature names. selected_features = function(lambda = NULL) { glmnet_selected_features(self, lambda) + }, + + #' @description + #' Returns importance scores, calculated from the path of lambda values. + #' First, the largest `lambda` at which the feature was first included in the model + #' with a nonzero coefficient is determined. + #' Second, the [rank()] of these lambda values is calculated (using averaging for ties) + #' and returned as importance scores. + #' + #' @return (named `numeric()`) of importance scores. + importance = function() { + glmnet_importance(self) } ), diff --git a/R/LearnerSurvGlmnet.R b/R/LearnerSurvGlmnet.R index e06cfde3..fe1d4f7b 100644 --- a/R/LearnerSurvGlmnet.R +++ b/R/LearnerSurvGlmnet.R @@ -74,7 +74,7 @@ LearnerSurvGlmnet = R6Class("LearnerSurvGlmnet", param_set = ps, feature_types = c("logical", "integer", "numeric"), predict_types = c("crank", "lp"), - properties = c("weights", "selected_features"), + properties = c("importance", "selected_features", "weights"), packages = c("mlr3learners", "glmnet"), man = "mlr3learners::mlr_learners_surv.glmnet" ) @@ -90,6 +90,18 @@ LearnerSurvGlmnet = R6Class("LearnerSurvGlmnet", #' @return (`character()`) of feature names. selected_features = function(lambda = NULL) { glmnet_selected_features(self, lambda) + }, + + #' @description + #' Returns importance scores, calculated from the path of lambda values. + #' First, the largest `lambda` at which the feature was first included in the model + #' with a nonzero coefficient is determined. + #' Second, the [rank()] of these lambda values is calculated (using averaging for ties) + #' and returned as importance scores. + #' + #' @return (named `numeric()`) of importance scores. + importance = function() { + glmnet_importance(self) } ), diff --git a/R/helpers.R b/R/helpers.R index 3998bf76..6a29bbfd 100644 --- a/R/helpers.R +++ b/R/helpers.R @@ -1,3 +1,5 @@ +opts_default_contrasts = list(contrasts = c("contr.treatment", "contr.poly")) + # p = probability for levs[2] => matrix with probs for levs[1] and levs[2] pvec2mat = function(p, levs) { stopifnot(is.numeric(p)) @@ -43,6 +45,3 @@ extract_loglik = function(self) { } stats::logLik(self$model) } - - -opts_default_contrasts = list(contrasts = c("contr.treatment", "contr.poly")) diff --git a/R/helpers_glmnet.R b/R/helpers_glmnet.R index 969fb542..74356b33 100644 --- a/R/helpers_glmnet.R +++ b/R/helpers_glmnet.R @@ -53,6 +53,40 @@ glmnet_selected_features = function(self, lambda = NULL) { } +glmnet_importance = function(self) { + find_lambda = function(M) { + pos = apply(M, 1L, function(x) { + i = wf(x == 0, use.names = FALSE) + if (length(i)) i else Inf + }) + } + + model = self$model$glmnet.fit %??% self$model + lambdas = model$lambda + M = coef(model) + + if (is.list(M)) { + names(M) + rownames(M$virginica) + + } else { + # * remove intercept row + # * reorder with increasing lambda + M = M[rownames(M) != "(Intercept)", order(lambdas), drop = FALSE] + } + + + + # find position of smallest lambda with beta being penalized to 0 + pos = apply(M, 1L, function(x) { + i = wf(x == 0, use.names = FALSE) + if (length(i)) i else Inf + }) + + sort(rank(pos, ties.method = "average"), decreasing = TRUE) +} + + glmnet_invoke = function(data, target, pv, cv = FALSE) { saved_ctrl = glmnet::glmnet.control() on.exit(invoke(glmnet::glmnet.control, .args = saved_ctrl)) diff --git a/R/helpers_lin_models.R b/R/helpers_lin_models.R new file mode 100644 index 00000000..a277e2f5 --- /dev/null +++ b/R/helpers_lin_models.R @@ -0,0 +1,20 @@ +lin_model_importance = function(self) { + task = self$state$train_task + lvls = task$levels(task$feature_names, include_logicals = TRUE) + nlvls = lengths(lvls) + if (any(nlvls > 2L)) { + stopf("Importance cannot be extracted for models fitted on factors with more than 2 features") + } + + pvals = summary(self$model)$coefficients[, 4L] + pvals = pvals[names(pvals) != "(Intercept)"] + + # remove the appended 2nd level for binary factor levels + ii = (nlvls == 2L) + pvals = rename(pvals, + old = paste0(names(nlvls)[ii], map_chr(lvls[ii], tail, 1L)), + new = names(nlvls)[ii] + ) + + sort(-log10(pvals), decreasing = TRUE) +} diff --git a/man/mlr_learners_classif.cv_glmnet.Rd b/man/mlr_learners_classif.cv_glmnet.Rd index ddc6981c..738e4cbc 100644 --- a/man/mlr_learners_classif.cv_glmnet.Rd +++ b/man/mlr_learners_classif.cv_glmnet.Rd @@ -24,7 +24,7 @@ lrn("classif.cv_glmnet") \item Task type: \dQuote{classif} \item Predict Types: \dQuote{response}, \dQuote{prob} \item Feature Types: \dQuote{logical}, \dQuote{integer}, \dQuote{numeric} -\item Required Packages: \CRANpkg{mlr3learners}, \CRANpkg{glmnet} +\item Required Packages: \CRANpkg{mlr3}, \CRANpkg{mlr3learners}, \CRANpkg{glmnet} } } @@ -149,6 +149,7 @@ Other Learner: \itemize{ \item \href{#method-new}{\code{LearnerClassifCVGlmnet$new()}} \item \href{#method-selected_features}{\code{LearnerClassifCVGlmnet$selected_features()}} +\item \href{#method-importance}{\code{LearnerClassifCVGlmnet$importance()}} \item \href{#method-clone}{\code{LearnerClassifCVGlmnet$clone()}} } } @@ -199,6 +200,23 @@ Custom \code{lambda}, defaults to the active lambda depending on parameter set.} } } \if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-importance}{}}} +\subsection{Method \code{importance()}}{ +Returns importance scores, calculated from the path of lambda values. +First, the largest \code{lambda} at which the feature was first included in the model +with a nonzero coefficient is determined. +Second, the \code{\link[=rank]{rank()}} of these lambda values is calculated (using averaging for ties) +and returned as importance scores. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{LearnerClassifCVGlmnet$importance()}\if{html}{\out{
}} +} + +\subsection{Returns}{ +(named \code{numeric()}) of importance scores. +} +} +\if{html}{\out{
}} \if{html}{\out{}} \if{latex}{\out{\hypertarget{method-clone}{}}} \subsection{Method \code{clone()}}{ diff --git a/man/mlr_learners_classif.glmnet.Rd b/man/mlr_learners_classif.glmnet.Rd index ed1b6ba0..440734f0 100644 --- a/man/mlr_learners_classif.glmnet.Rd +++ b/man/mlr_learners_classif.glmnet.Rd @@ -39,7 +39,7 @@ lrn("classif.glmnet") \item Task type: \dQuote{classif} \item Predict Types: \dQuote{response}, \dQuote{prob} \item Feature Types: \dQuote{logical}, \dQuote{integer}, \dQuote{numeric} -\item Required Packages: \CRANpkg{mlr3learners}, \CRANpkg{glmnet} +\item Required Packages: \CRANpkg{mlr3}, \CRANpkg{mlr3learners}, \CRANpkg{glmnet} } } @@ -158,6 +158,7 @@ Other Learner: \itemize{ \item \href{#method-new}{\code{LearnerClassifGlmnet$new()}} \item \href{#method-selected_features}{\code{LearnerClassifGlmnet$selected_features()}} +\item \href{#method-importance}{\code{LearnerClassifGlmnet$importance()}} \item \href{#method-clone}{\code{LearnerClassifGlmnet$clone()}} } } @@ -208,6 +209,23 @@ Custom \code{lambda}, defaults to the active lambda depending on parameter set.} } } \if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-importance}{}}} +\subsection{Method \code{importance()}}{ +Returns importance scores, calculated from the path of lambda values. +First, the largest \code{lambda} at which the feature was first included in the model +with a nonzero coefficient is determined. +Second, the \code{\link[=rank]{rank()}} of these lambda values is calculated (using averaging for ties) +and returned as importance scores. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{LearnerClassifGlmnet$importance()}\if{html}{\out{
}} +} + +\subsection{Returns}{ +(named \code{numeric()}) of importance scores. +} +} +\if{html}{\out{
}} \if{html}{\out{}} \if{latex}{\out{\hypertarget{method-clone}{}}} \subsection{Method \code{clone()}}{ diff --git a/man/mlr_learners_classif.kknn.Rd b/man/mlr_learners_classif.kknn.Rd index 1528a3cd..1aaf553e 100644 --- a/man/mlr_learners_classif.kknn.Rd +++ b/man/mlr_learners_classif.kknn.Rd @@ -32,7 +32,7 @@ lrn("classif.kknn") \item Task type: \dQuote{classif} \item Predict Types: \dQuote{response}, \dQuote{prob} \item Feature Types: \dQuote{logical}, \dQuote{integer}, \dQuote{numeric}, \dQuote{factor}, \dQuote{ordered} -\item Required Packages: \CRANpkg{mlr3learners}, \CRANpkg{kknn} +\item Required Packages: \CRANpkg{mlr3}, \CRANpkg{mlr3learners}, \CRANpkg{kknn} } } diff --git a/man/mlr_learners_classif.lda.Rd b/man/mlr_learners_classif.lda.Rd index 1f2058b3..c5a15a5a 100644 --- a/man/mlr_learners_classif.lda.Rd +++ b/man/mlr_learners_classif.lda.Rd @@ -27,7 +27,7 @@ lrn("classif.lda") \item Task type: \dQuote{classif} \item Predict Types: \dQuote{response}, \dQuote{prob} \item Feature Types: \dQuote{logical}, \dQuote{integer}, \dQuote{numeric}, \dQuote{factor}, \dQuote{ordered} -\item Required Packages: \CRANpkg{mlr3learners}, \CRANpkg{MASS} +\item Required Packages: \CRANpkg{mlr3}, \CRANpkg{mlr3learners}, \CRANpkg{MASS} } } diff --git a/man/mlr_learners_classif.log_reg.Rd b/man/mlr_learners_classif.log_reg.Rd index b2e35801..95a83cc6 100644 --- a/man/mlr_learners_classif.log_reg.Rd +++ b/man/mlr_learners_classif.log_reg.Rd @@ -40,7 +40,7 @@ lrn("classif.log_reg") \item Task type: \dQuote{classif} \item Predict Types: \dQuote{response}, \dQuote{prob} \item Feature Types: \dQuote{logical}, \dQuote{integer}, \dQuote{numeric}, \dQuote{character}, \dQuote{factor}, \dQuote{ordered} -\item Required Packages: \CRANpkg{mlr3learners}, 'stats' +\item Required Packages: \CRANpkg{mlr3}, \CRANpkg{mlr3learners}, 'stats' } } @@ -134,6 +134,7 @@ Other Learner: \itemize{ \item \href{#method-new}{\code{LearnerClassifLogReg$new()}} \item \href{#method-loglik}{\code{LearnerClassifLogReg$loglik()}} +\item \href{#method-importance}{\code{LearnerClassifLogReg$importance()}} \item \href{#method-clone}{\code{LearnerClassifLogReg$clone()}} } } @@ -170,6 +171,21 @@ Extract the log-likelihood (e.g., via \code{\link[stats:logLik]{stats::logLik()} \if{html}{\out{
}}\preformatted{LearnerClassifLogReg$loglik()}\if{html}{\out{
}} } +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-importance}{}}} +\subsection{Method \code{importance()}}{ +Importance scores as \eqn{-log_{10}()}{-log10()} transformed \eqn{p}-values, +extracted from \code{\link[=summary]{summary()}}. +Does not work if the model has been fitted on factor features with more than 2 levels. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{LearnerClassifLogReg$importance()}\if{html}{\out{
}} +} + +\subsection{Returns}{ +Named \code{numeric()}. +} } \if{html}{\out{
}} \if{html}{\out{}} diff --git a/man/mlr_learners_classif.multinom.Rd b/man/mlr_learners_classif.multinom.Rd index 14e8baf6..b468d5f7 100644 --- a/man/mlr_learners_classif.multinom.Rd +++ b/man/mlr_learners_classif.multinom.Rd @@ -21,7 +21,7 @@ lrn("classif.multinom") \item Task type: \dQuote{classif} \item Predict Types: \dQuote{response}, \dQuote{prob} \item Feature Types: \dQuote{logical}, \dQuote{integer}, \dQuote{numeric}, \dQuote{factor} -\item Required Packages: \CRANpkg{mlr3learners}, \CRANpkg{nnet} +\item Required Packages: \CRANpkg{mlr3}, \CRANpkg{mlr3learners}, \CRANpkg{nnet} } } diff --git a/man/mlr_learners_classif.naive_bayes.Rd b/man/mlr_learners_classif.naive_bayes.Rd index 17f2f494..dbb7da65 100644 --- a/man/mlr_learners_classif.naive_bayes.Rd +++ b/man/mlr_learners_classif.naive_bayes.Rd @@ -21,7 +21,7 @@ lrn("classif.naive_bayes") \item Task type: \dQuote{classif} \item Predict Types: \dQuote{response}, \dQuote{prob} \item Feature Types: \dQuote{logical}, \dQuote{integer}, \dQuote{numeric}, \dQuote{factor} -\item Required Packages: \CRANpkg{mlr3learners}, \CRANpkg{e1071} +\item Required Packages: \CRANpkg{mlr3}, \CRANpkg{mlr3learners}, \CRANpkg{e1071} } } diff --git a/man/mlr_learners_classif.nnet.Rd b/man/mlr_learners_classif.nnet.Rd index c675b924..59c01f5c 100644 --- a/man/mlr_learners_classif.nnet.Rd +++ b/man/mlr_learners_classif.nnet.Rd @@ -24,7 +24,7 @@ lrn("classif.nnet") \item Task type: \dQuote{classif} \item Predict Types: \dQuote{prob}, \dQuote{response} \item Feature Types: \dQuote{numeric}, \dQuote{factor}, \dQuote{ordered} -\item Required Packages: \CRANpkg{mlr3learners}, \CRANpkg{nnet} +\item Required Packages: \CRANpkg{mlr3}, \CRANpkg{mlr3learners}, \CRANpkg{nnet} } } diff --git a/man/mlr_learners_classif.qda.Rd b/man/mlr_learners_classif.qda.Rd index 1b47b149..e8e9e89f 100644 --- a/man/mlr_learners_classif.qda.Rd +++ b/man/mlr_learners_classif.qda.Rd @@ -27,7 +27,7 @@ lrn("classif.qda") \item Task type: \dQuote{classif} \item Predict Types: \dQuote{response}, \dQuote{prob} \item Feature Types: \dQuote{logical}, \dQuote{integer}, \dQuote{numeric}, \dQuote{factor}, \dQuote{ordered} -\item Required Packages: \CRANpkg{mlr3learners}, \CRANpkg{MASS} +\item Required Packages: \CRANpkg{mlr3}, \CRANpkg{mlr3learners}, \CRANpkg{MASS} } } diff --git a/man/mlr_learners_classif.ranger.Rd b/man/mlr_learners_classif.ranger.Rd index b5dba635..2e8f8729 100644 --- a/man/mlr_learners_classif.ranger.Rd +++ b/man/mlr_learners_classif.ranger.Rd @@ -39,7 +39,7 @@ lrn("classif.ranger") \item Task type: \dQuote{classif} \item Predict Types: \dQuote{response}, \dQuote{prob} \item Feature Types: \dQuote{logical}, \dQuote{integer}, \dQuote{numeric}, \dQuote{character}, \dQuote{factor}, \dQuote{ordered} -\item Required Packages: \CRANpkg{mlr3learners}, \CRANpkg{ranger} +\item Required Packages: \CRANpkg{mlr3}, \CRANpkg{mlr3learners}, \CRANpkg{ranger} } } diff --git a/man/mlr_learners_classif.svm.Rd b/man/mlr_learners_classif.svm.Rd index 7946ee2d..98667f92 100644 --- a/man/mlr_learners_classif.svm.Rd +++ b/man/mlr_learners_classif.svm.Rd @@ -21,7 +21,7 @@ lrn("classif.svm") \item Task type: \dQuote{classif} \item Predict Types: \dQuote{response}, \dQuote{prob} \item Feature Types: \dQuote{logical}, \dQuote{integer}, \dQuote{numeric} -\item Required Packages: \CRANpkg{mlr3learners}, \CRANpkg{e1071} +\item Required Packages: \CRANpkg{mlr3}, \CRANpkg{mlr3learners}, \CRANpkg{e1071} } } diff --git a/man/mlr_learners_classif.xgboost.Rd b/man/mlr_learners_classif.xgboost.Rd index 38c753d6..a08baecd 100644 --- a/man/mlr_learners_classif.xgboost.Rd +++ b/man/mlr_learners_classif.xgboost.Rd @@ -47,7 +47,7 @@ lrn("classif.xgboost") \item Task type: \dQuote{classif} \item Predict Types: \dQuote{response}, \dQuote{prob} \item Feature Types: \dQuote{logical}, \dQuote{integer}, \dQuote{numeric} -\item Required Packages: \CRANpkg{mlr3learners}, \CRANpkg{xgboost} +\item Required Packages: \CRANpkg{mlr3}, \CRANpkg{mlr3learners}, \CRANpkg{xgboost} } } diff --git a/man/mlr_learners_regr.cv_glmnet.Rd b/man/mlr_learners_regr.cv_glmnet.Rd index 7ad2164e..341acdb9 100644 --- a/man/mlr_learners_regr.cv_glmnet.Rd +++ b/man/mlr_learners_regr.cv_glmnet.Rd @@ -23,7 +23,7 @@ lrn("regr.cv_glmnet") \item Task type: \dQuote{regr} \item Predict Types: \dQuote{response} \item Feature Types: \dQuote{logical}, \dQuote{integer}, \dQuote{numeric} -\item Required Packages: \CRANpkg{mlr3learners}, \CRANpkg{glmnet} +\item Required Packages: \CRANpkg{mlr3}, \CRANpkg{mlr3learners}, \CRANpkg{glmnet} } } @@ -142,6 +142,7 @@ Other Learner: \itemize{ \item \href{#method-new}{\code{LearnerRegrCVGlmnet$new()}} \item \href{#method-selected_features}{\code{LearnerRegrCVGlmnet$selected_features()}} +\item \href{#method-importance}{\code{LearnerRegrCVGlmnet$importance()}} \item \href{#method-clone}{\code{LearnerRegrCVGlmnet$clone()}} } } @@ -192,6 +193,23 @@ Custom \code{lambda}, defaults to the active lambda depending on parameter set.} } } \if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-importance}{}}} +\subsection{Method \code{importance()}}{ +Returns importance scores, calculated from the path of lambda values. +First, the largest \code{lambda} at which the feature was first included in the model +with a nonzero coefficient is determined. +Second, the \code{\link[=rank]{rank()}} of these lambda values is calculated (using averaging for ties) +and returned as importance scores. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{LearnerRegrCVGlmnet$importance()}\if{html}{\out{
}} +} + +\subsection{Returns}{ +(named \code{numeric()}) of importance scores. +} +} +\if{html}{\out{
}} \if{html}{\out{}} \if{latex}{\out{\hypertarget{method-clone}{}}} \subsection{Method \code{clone()}}{ diff --git a/man/mlr_learners_regr.glmnet.Rd b/man/mlr_learners_regr.glmnet.Rd index ab6dbc07..d79d5916 100644 --- a/man/mlr_learners_regr.glmnet.Rd +++ b/man/mlr_learners_regr.glmnet.Rd @@ -41,7 +41,7 @@ lrn("regr.glmnet") \item Task type: \dQuote{regr} \item Predict Types: \dQuote{response} \item Feature Types: \dQuote{logical}, \dQuote{integer}, \dQuote{numeric} -\item Required Packages: \CRANpkg{mlr3learners}, \CRANpkg{glmnet} +\item Required Packages: \CRANpkg{mlr3}, \CRANpkg{mlr3learners}, \CRANpkg{glmnet} } } @@ -158,6 +158,7 @@ Other Learner: \itemize{ \item \href{#method-new}{\code{LearnerRegrGlmnet$new()}} \item \href{#method-selected_features}{\code{LearnerRegrGlmnet$selected_features()}} +\item \href{#method-importance}{\code{LearnerRegrGlmnet$importance()}} \item \href{#method-clone}{\code{LearnerRegrGlmnet$clone()}} } } @@ -208,6 +209,23 @@ Custom \code{lambda}, defaults to the active lambda depending on parameter set.} } } \if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-importance}{}}} +\subsection{Method \code{importance()}}{ +Returns importance scores, calculated from the path of lambda values. +First, the largest \code{lambda} at which the feature was first included in the model +with a nonzero coefficient is determined. +Second, the \code{\link[=rank]{rank()}} of these lambda values is calculated (using averaging for ties) +and returned as importance scores. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{LearnerRegrGlmnet$importance()}\if{html}{\out{
}} +} + +\subsection{Returns}{ +(named \code{numeric()}) of importance scores. +} +} +\if{html}{\out{
}} \if{html}{\out{}} \if{latex}{\out{\hypertarget{method-clone}{}}} \subsection{Method \code{clone()}}{ diff --git a/man/mlr_learners_regr.kknn.Rd b/man/mlr_learners_regr.kknn.Rd index 3c2ced51..d22a5a67 100644 --- a/man/mlr_learners_regr.kknn.Rd +++ b/man/mlr_learners_regr.kknn.Rd @@ -32,7 +32,7 @@ lrn("regr.kknn") \item Task type: \dQuote{regr} \item Predict Types: \dQuote{response} \item Feature Types: \dQuote{logical}, \dQuote{integer}, \dQuote{numeric}, \dQuote{factor}, \dQuote{ordered} -\item Required Packages: \CRANpkg{mlr3learners}, \CRANpkg{kknn} +\item Required Packages: \CRANpkg{mlr3}, \CRANpkg{mlr3learners}, \CRANpkg{kknn} } } diff --git a/man/mlr_learners_regr.km.Rd b/man/mlr_learners_regr.km.Rd index 5e17158b..4e1dd23e 100644 --- a/man/mlr_learners_regr.km.Rd +++ b/man/mlr_learners_regr.km.Rd @@ -30,7 +30,7 @@ lrn("regr.km") \item Task type: \dQuote{regr} \item Predict Types: \dQuote{response}, \dQuote{se} \item Feature Types: \dQuote{logical}, \dQuote{integer}, \dQuote{numeric} -\item Required Packages: \CRANpkg{mlr3learners}, \CRANpkg{DiceKriging} +\item Required Packages: \CRANpkg{mlr3}, \CRANpkg{mlr3learners}, \CRANpkg{DiceKriging} } } diff --git a/man/mlr_learners_regr.lm.Rd b/man/mlr_learners_regr.lm.Rd index aa3b97ac..9b1ce62d 100644 --- a/man/mlr_learners_regr.lm.Rd +++ b/man/mlr_learners_regr.lm.Rd @@ -21,7 +21,7 @@ lrn("regr.lm") \item Task type: \dQuote{regr} \item Predict Types: \dQuote{response}, \dQuote{se} \item Feature Types: \dQuote{logical}, \dQuote{integer}, \dQuote{numeric}, \dQuote{factor}, \dQuote{character} -\item Required Packages: \CRANpkg{mlr3learners}, 'stats' +\item Required Packages: \CRANpkg{mlr3}, \CRANpkg{mlr3learners}, 'stats' } } @@ -114,6 +114,7 @@ Other Learner: \itemize{ \item \href{#method-new}{\code{LearnerRegrLM$new()}} \item \href{#method-loglik}{\code{LearnerRegrLM$loglik()}} +\item \href{#method-importance}{\code{LearnerRegrLM$importance()}} \item \href{#method-clone}{\code{LearnerRegrLM$clone()}} } } @@ -150,6 +151,21 @@ Extract the log-likelihood (e.g., via \code{\link[stats:logLik]{stats::logLik()} \if{html}{\out{
}}\preformatted{LearnerRegrLM$loglik()}\if{html}{\out{
}} } +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-importance}{}}} +\subsection{Method \code{importance()}}{ +Importance scores as \eqn{-log_{10}()}{-log10()} transformed \eqn{p}-values, +extracted from \code{\link[=summary]{summary()}}. +Does not work if the model has been fitted on factor features with more than 2 levels. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{LearnerRegrLM$importance()}\if{html}{\out{
}} +} + +\subsection{Returns}{ +Named \code{numeric()}. +} } \if{html}{\out{
}} \if{html}{\out{}} diff --git a/man/mlr_learners_regr.ranger.Rd b/man/mlr_learners_regr.ranger.Rd index 61a44955..eb4c7e05 100644 --- a/man/mlr_learners_regr.ranger.Rd +++ b/man/mlr_learners_regr.ranger.Rd @@ -21,7 +21,7 @@ lrn("regr.ranger") \item Task type: \dQuote{regr} \item Predict Types: \dQuote{response}, \dQuote{se} \item Feature Types: \dQuote{logical}, \dQuote{integer}, \dQuote{numeric}, \dQuote{character}, \dQuote{factor}, \dQuote{ordered} -\item Required Packages: \CRANpkg{mlr3learners}, \CRANpkg{ranger} +\item Required Packages: \CRANpkg{mlr3}, \CRANpkg{mlr3learners}, \CRANpkg{ranger} } } diff --git a/man/mlr_learners_regr.svm.Rd b/man/mlr_learners_regr.svm.Rd index 1d0873c8..1e7a42f2 100644 --- a/man/mlr_learners_regr.svm.Rd +++ b/man/mlr_learners_regr.svm.Rd @@ -21,7 +21,7 @@ lrn("regr.svm") \item Task type: \dQuote{regr} \item Predict Types: \dQuote{response} \item Feature Types: \dQuote{logical}, \dQuote{integer}, \dQuote{numeric} -\item Required Packages: \CRANpkg{mlr3learners}, \CRANpkg{e1071} +\item Required Packages: \CRANpkg{mlr3}, \CRANpkg{mlr3learners}, \CRANpkg{e1071} } } diff --git a/man/mlr_learners_regr.xgboost.Rd b/man/mlr_learners_regr.xgboost.Rd index b4c02a7c..0cec28ea 100644 --- a/man/mlr_learners_regr.xgboost.Rd +++ b/man/mlr_learners_regr.xgboost.Rd @@ -21,7 +21,7 @@ lrn("regr.xgboost") \item Task type: \dQuote{regr} \item Predict Types: \dQuote{response} \item Feature Types: \dQuote{logical}, \dQuote{integer}, \dQuote{numeric} -\item Required Packages: \CRANpkg{mlr3learners}, \CRANpkg{xgboost} +\item Required Packages: \CRANpkg{mlr3}, \CRANpkg{mlr3learners}, \CRANpkg{xgboost} } } diff --git a/man/mlr_learners_surv.cv_glmnet.Rd b/man/mlr_learners_surv.cv_glmnet.Rd index 27284481..49df0c14 100644 --- a/man/mlr_learners_surv.cv_glmnet.Rd +++ b/man/mlr_learners_surv.cv_glmnet.Rd @@ -23,7 +23,7 @@ lrn("surv.cv_glmnet") \item Task type: \dQuote{surv} \item Predict Types: \dQuote{crank}, \dQuote{lp} \item Feature Types: \dQuote{logical}, \dQuote{integer}, \dQuote{numeric} -\item Required Packages: \CRANpkg{mlr3learners}, \CRANpkg{glmnet} +\item Required Packages: \CRANpkg{mlr3}, \CRANpkg{mlr3learners}, \CRANpkg{glmnet} } } @@ -141,6 +141,7 @@ Other Learner: \itemize{ \item \href{#method-new}{\code{LearnerSurvCVGlmnet$new()}} \item \href{#method-selected_features}{\code{LearnerSurvCVGlmnet$selected_features()}} +\item \href{#method-importance}{\code{LearnerSurvCVGlmnet$importance()}} \item \href{#method-clone}{\code{LearnerSurvCVGlmnet$clone()}} } } @@ -191,6 +192,23 @@ Custom \code{lambda}, defaults to the active lambda depending on parameter set.} } } \if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-importance}{}}} +\subsection{Method \code{importance()}}{ +Returns importance scores, calculated from the path of lambda values. +First, the largest \code{lambda} at which the feature was first included in the model +with a nonzero coefficient is determined. +Second, the \code{\link[=rank]{rank()}} of these lambda values is calculated (using averaging for ties) +and returned as importance scores. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{LearnerSurvCVGlmnet$importance()}\if{html}{\out{
}} +} + +\subsection{Returns}{ +(named \code{numeric()}) of importance scores. +} +} +\if{html}{\out{
}} \if{html}{\out{}} \if{latex}{\out{\hypertarget{method-clone}{}}} \subsection{Method \code{clone()}}{ diff --git a/man/mlr_learners_surv.glmnet.Rd b/man/mlr_learners_surv.glmnet.Rd index 1b7c61b2..0c117b85 100644 --- a/man/mlr_learners_surv.glmnet.Rd +++ b/man/mlr_learners_surv.glmnet.Rd @@ -41,7 +41,7 @@ lrn("surv.glmnet") \item Task type: \dQuote{surv} \item Predict Types: \dQuote{crank}, \dQuote{lp} \item Feature Types: \dQuote{logical}, \dQuote{integer}, \dQuote{numeric} -\item Required Packages: \CRANpkg{mlr3learners}, \CRANpkg{glmnet} +\item Required Packages: \CRANpkg{mlr3}, \CRANpkg{mlr3learners}, \CRANpkg{glmnet} } } @@ -156,6 +156,7 @@ Other Learner: \itemize{ \item \href{#method-new}{\code{LearnerSurvGlmnet$new()}} \item \href{#method-selected_features}{\code{LearnerSurvGlmnet$selected_features()}} +\item \href{#method-importance}{\code{LearnerSurvGlmnet$importance()}} \item \href{#method-clone}{\code{LearnerSurvGlmnet$clone()}} } } @@ -206,6 +207,23 @@ Custom \code{lambda}, defaults to the active lambda depending on parameter set.} } } \if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-importance}{}}} +\subsection{Method \code{importance()}}{ +Returns importance scores, calculated from the path of lambda values. +First, the largest \code{lambda} at which the feature was first included in the model +with a nonzero coefficient is determined. +Second, the \code{\link[=rank]{rank()}} of these lambda values is calculated (using averaging for ties) +and returned as importance scores. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{LearnerSurvGlmnet$importance()}\if{html}{\out{
}} +} + +\subsection{Returns}{ +(named \code{numeric()}) of importance scores. +} +} +\if{html}{\out{
}} \if{html}{\out{}} \if{latex}{\out{\hypertarget{method-clone}{}}} \subsection{Method \code{clone()}}{ diff --git a/man/mlr_learners_surv.ranger.Rd b/man/mlr_learners_surv.ranger.Rd index 7c7323fb..bff9ce38 100644 --- a/man/mlr_learners_surv.ranger.Rd +++ b/man/mlr_learners_surv.ranger.Rd @@ -21,7 +21,7 @@ lrn("surv.ranger") \item Task type: \dQuote{surv} \item Predict Types: \dQuote{distr}, \dQuote{crank} \item Feature Types: \dQuote{logical}, \dQuote{integer}, \dQuote{numeric}, \dQuote{character}, \dQuote{factor}, \dQuote{ordered} -\item Required Packages: \CRANpkg{mlr3learners}, \CRANpkg{ranger} +\item Required Packages: \CRANpkg{mlr3}, \CRANpkg{mlr3learners}, \CRANpkg{ranger} } } diff --git a/man/mlr_learners_surv.xgboost.Rd b/man/mlr_learners_surv.xgboost.Rd index 9de953cc..3461f1b9 100644 --- a/man/mlr_learners_surv.xgboost.Rd +++ b/man/mlr_learners_surv.xgboost.Rd @@ -53,7 +53,7 @@ lrn("surv.xgboost") \item Task type: \dQuote{surv} \item Predict Types: \dQuote{crank}, \dQuote{lp} \item Feature Types: \dQuote{integer}, \dQuote{numeric} -\item Required Packages: \CRANpkg{mlr3learners}, \CRANpkg{xgboost} +\item Required Packages: \CRANpkg{mlr3}, \CRANpkg{mlr3learners}, \CRANpkg{xgboost} } } diff --git a/tests/testthat/test_classif_cv_glmnet.R b/tests/testthat/test_classif_cv_glmnet.R index b568f2a5..1f3a5f40 100644 --- a/tests/testthat/test_classif_cv_glmnet.R +++ b/tests/testthat/test_classif_cv_glmnet.R @@ -8,6 +8,10 @@ test_that("autotest", { skip_on_os("solaris") result = run_autotest(learner, exclude = "feat_single") expect_true(result, info = result$error) + + + learner = result$learner + learner$importance() }) test_that("prob column reordering (#155)", { diff --git a/tests/testthat/test_regr_glmnet.R b/tests/testthat/test_regr_glmnet.R index 54041a56..65c30a38 100644 --- a/tests/testthat/test_regr_glmnet.R +++ b/tests/testthat/test_regr_glmnet.R @@ -10,7 +10,7 @@ test_that("autotest", { expect_true(result, info = result$error) }) -test_that("selected_features", { +test_that("selected_features + importance", { task = tsk("mtcars") learner = lrn("regr.glmnet") learner$train(task) @@ -24,4 +24,10 @@ test_that("selected_features", { learner$selected_features(Inf), character() ) + + expect_numeric( + learner$importance(), + any.missing = FALSE, + names = "unique" + ) }) diff --git a/tests/testthat/test_surv_glmnet.R b/tests/testthat/test_surv_glmnet.R index e06e42b8..9850bab3 100644 --- a/tests/testthat/test_surv_glmnet.R +++ b/tests/testthat/test_surv_glmnet.R @@ -12,7 +12,7 @@ test_that("autotest", { test_that("selected_features", { task = tsk("gbcs") - learner = lrn("surv.cv_glmnet") + learner = lrn("surv.glmnet") learner$train(task) expect_equal( From 53627b7b08ac0fb926181083d94ab7a1d8017dc6 Mon Sep 17 00:00:00 2001 From: Michel Lang Date: Thu, 18 Nov 2021 13:51:49 +0100 Subject: [PATCH 2/4] repair summary.multinom() --- R/LearnerClassifMultinom.R | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/R/LearnerClassifMultinom.R b/R/LearnerClassifMultinom.R index 7f12ec84..a1db0d03 100644 --- a/R/LearnerClassifMultinom.R +++ b/R/LearnerClassifMultinom.R @@ -62,16 +62,20 @@ LearnerClassifMultinom = R6Class("LearnerClassifMultinom", private = list( .train = function(task) { pv = self$param_set$get_values(tags = "train") - data = task$data() if ("weights" %in% task$properties) { pv$weights = task$weights$weight } + if (!is.null(pv$summ)) { pv$summ = as.integer(pv$summ) } - invoke(nnet::multinom, data = data, .args = pv) + # nnet does not handle formulas without env, we need to create it + # here to work with `summary()`. + pv$formula = reformulate(".", response = task$target_names) + + invoke(nnet::multinom, data = task$data(), .args = pv) }, .predict = function(task) { From 6865a257cd96ebb78c4cc24e5036988fd812cd3e Mon Sep 17 00:00:00 2001 From: Michel Lang Date: Thu, 18 Nov 2021 13:54:18 +0100 Subject: [PATCH 3/4] ... --- NAMESPACE | 2 + R/zzz.R | 2 +- man/mlr_learners_classif.cv_glmnet.Rd | 38 ++++++++-------- man/mlr_learners_classif.glmnet.Rd | 28 ++++++------ man/mlr_learners_classif.kknn.Rd | 6 +-- man/mlr_learners_classif.lda.Rd | 10 ++--- man/mlr_learners_classif.log_reg.Rd | 20 ++++----- man/mlr_learners_classif.multinom.Rd | 22 ++++----- man/mlr_learners_classif.nnet.Rd | 18 ++++---- man/mlr_learners_classif.qda.Rd | 8 ++-- man/mlr_learners_classif.ranger.Rd | 34 +++++++------- man/mlr_learners_classif.svm.Rd | 14 +++--- man/mlr_learners_classif.xgboost.Rd | 64 +++++++++++++-------------- man/mlr_learners_regr.cv_glmnet.Rd | 40 ++++++++--------- man/mlr_learners_regr.glmnet.Rd | 38 ++++++++-------- man/mlr_learners_regr.kknn.Rd | 6 +-- man/mlr_learners_regr.km.Rd | 48 ++++++++++---------- man/mlr_learners_regr.lm.Rd | 16 +++---- man/mlr_learners_regr.ranger.Rd | 34 +++++++------- man/mlr_learners_regr.svm.Rd | 10 ++--- man/mlr_learners_regr.xgboost.Rd | 64 +++++++++++++-------------- man/mlr_learners_surv.cv_glmnet.Rd | 40 ++++++++--------- man/mlr_learners_surv.glmnet.Rd | 36 +++++++-------- man/mlr_learners_surv.ranger.Rd | 28 ++++++------ man/mlr_learners_surv.xgboost.Rd | 48 ++++++++++---------- 25 files changed, 338 insertions(+), 336 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 5d92df5d..b62105c6 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -32,5 +32,7 @@ importFrom(R6,R6Class) importFrom(mlr3,LearnerClassif) importFrom(mlr3,LearnerRegr) importFrom(mlr3,mlr_learners) +importFrom(stats,coef) importFrom(stats,predict) +importFrom(stats,tail) importFrom(utils,bibentry) diff --git a/R/zzz.R b/R/zzz.R index 2ed7c3b4..582032a1 100644 --- a/R/zzz.R +++ b/R/zzz.R @@ -4,7 +4,7 @@ #' @import checkmate #' @importFrom R6 R6Class #' @importFrom mlr3 mlr_learners LearnerClassif LearnerRegr -#' @importFrom stats predict +#' @importFrom stats predict coef tail #' #' @description #' More learners are implemented in the [mlr3extralearners package](https://github.com/mlr-org/mlr3extralearners). diff --git a/man/mlr_learners_classif.cv_glmnet.Rd b/man/mlr_learners_classif.cv_glmnet.Rd index 738e4cbc..656ad03f 100644 --- a/man/mlr_learners_classif.cv_glmnet.Rd +++ b/man/mlr_learners_classif.cv_glmnet.Rd @@ -31,7 +31,7 @@ lrn("classif.cv_glmnet") \section{Parameters}{ \tabular{lllll}{ Id \tab Type \tab Default \tab Levels \tab Range \cr - alignment \tab character \tab lambda \tab lambda, fraction \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + alignment \tab character \tab lambda \tab lambda, fraction \tab - \cr alpha \tab numeric \tab 1 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr big \tab numeric \tab 9.9e+35 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr devmax \tab numeric \tab 0.999 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr @@ -41,38 +41,38 @@ lrn("classif.cv_glmnet") exclude \tab integer \tab - \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr exmx \tab numeric \tab 250 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr fdev \tab numeric \tab 1e-05 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - foldid \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - gamma \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - grouped \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - intercept \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - keep \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + foldid \tab list \tab NULL \tab \tab - \cr + gamma \tab list \tab - \tab \tab - \cr + grouped \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + intercept \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + keep \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr lambda.min.ratio \tab numeric \tab - \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - lambda \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - lower.limits \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + lambda \tab list \tab - \tab \tab - \cr + lower.limits \tab list \tab - \tab \tab - \cr maxit \tab integer \tab 100000 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr mnlam \tab integer \tab 5 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr mxitnr \tab integer \tab 25 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr mxit \tab integer \tab 100 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr nfolds \tab integer \tab 10 \tab \tab \eqn{[3, \infty)}{[3, Inf)} \cr nlambda \tab integer \tab 100 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr - offset \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - parallel \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - penalty.factor \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + offset \tab list \tab NULL \tab \tab - \cr + parallel \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + penalty.factor \tab list \tab - \tab \tab - \cr pmax \tab integer \tab - \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr pmin \tab numeric \tab 1e-09 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr prec \tab numeric \tab 1e-10 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr predict.gamma \tab numeric \tab gamma.1se \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - relax \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + relax \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr s \tab numeric \tab lambda.1se \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - standardize \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - standardize.response \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + standardize \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + standardize.response \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr thresh \tab numeric \tab 1e-07 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr trace.it \tab integer \tab 0 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - type.gaussian \tab character \tab - \tab covariance, naive \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - type.logistic \tab character \tab - \tab Newton, modified.Newton \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - type.measure \tab character \tab deviance \tab deviance, class, auc, mse, mae \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - type.multinomial \tab character \tab - \tab ungrouped, grouped \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - upper.limits \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + type.gaussian \tab character \tab - \tab covariance, naive \tab - \cr + type.logistic \tab character \tab - \tab Newton, modified.Newton \tab - \cr + type.measure \tab character \tab deviance \tab deviance, class, auc, mse, mae \tab - \cr + type.multinomial \tab character \tab - \tab ungrouped, grouped \tab - \cr + upper.limits \tab list \tab - \tab \tab - \cr } } diff --git a/man/mlr_learners_classif.glmnet.Rd b/man/mlr_learners_classif.glmnet.Rd index 440734f0..5a00f7a2 100644 --- a/man/mlr_learners_classif.glmnet.Rd +++ b/man/mlr_learners_classif.glmnet.Rd @@ -52,36 +52,36 @@ lrn("classif.glmnet") dfmax \tab integer \tab - \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr eps \tab numeric \tab 1e-06 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr epsnr \tab numeric \tab 1e-08 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - exact \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + exact \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr exclude \tab integer \tab - \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr exmx \tab numeric \tab 250 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr fdev \tab numeric \tab 1e-05 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr gamma \tab numeric \tab 1 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - intercept \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - lambda \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + intercept \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + lambda \tab list \tab - \tab \tab - \cr lambda.min.ratio \tab numeric \tab - \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - lower.limits \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + lower.limits \tab list \tab - \tab \tab - \cr maxit \tab integer \tab 100000 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr mnlam \tab integer \tab 5 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr mxit \tab integer \tab 100 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr mxitnr \tab integer \tab 25 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr nlambda \tab integer \tab 100 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr - newoffset \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - offset \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - penalty.factor \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + newoffset \tab list \tab - \tab \tab - \cr + offset \tab list \tab NULL \tab \tab - \cr + penalty.factor \tab list \tab - \tab \tab - \cr pmax \tab integer \tab - \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr pmin \tab numeric \tab 1e-09 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr prec \tab numeric \tab 1e-10 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - relax \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + relax \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr s \tab numeric \tab 0.01 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - standardize \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - standardize.response \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + standardize \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + standardize.response \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr thresh \tab numeric \tab 1e-07 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr trace.it \tab integer \tab 0 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - type.gaussian \tab character \tab - \tab covariance, naive \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - type.logistic \tab character \tab - \tab Newton, modified.Newton \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - type.multinomial \tab character \tab - \tab ungrouped, grouped \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - upper.limits \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + type.gaussian \tab character \tab - \tab covariance, naive \tab - \cr + type.logistic \tab character \tab - \tab Newton, modified.Newton \tab - \cr + type.multinomial \tab character \tab - \tab ungrouped, grouped \tab - \cr + upper.limits \tab list \tab - \tab \tab - \cr } } diff --git a/man/mlr_learners_classif.kknn.Rd b/man/mlr_learners_classif.kknn.Rd index 1aaf553e..3cda9d02 100644 --- a/man/mlr_learners_classif.kknn.Rd +++ b/man/mlr_learners_classif.kknn.Rd @@ -41,9 +41,9 @@ lrn("classif.kknn") Id \tab Type \tab Default \tab Levels \tab Range \cr k \tab integer \tab 7 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr distance \tab numeric \tab 2 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - kernel \tab character \tab optimal \tab rectangular, triangular, epanechnikov, biweight, triweight, cos, inv, gaussian, rank, optimal \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - scale \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - ykernel \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + kernel \tab character \tab optimal \tab rectangular, triangular, epanechnikov, biweight, triweight, cos, inv, gaussian, rank, optimal \tab - \cr + scale \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + ykernel \tab list \tab NULL \tab \tab - \cr } } diff --git a/man/mlr_learners_classif.lda.Rd b/man/mlr_learners_classif.lda.Rd index c5a15a5a..e1c9fc41 100644 --- a/man/mlr_learners_classif.lda.Rd +++ b/man/mlr_learners_classif.lda.Rd @@ -34,12 +34,12 @@ lrn("classif.lda") \section{Parameters}{ \tabular{lllll}{ Id \tab Type \tab Default \tab Levels \tab Range \cr - dimen \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - method \tab character \tab moment \tab moment, mle, mve, t \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + dimen \tab list \tab - \tab \tab - \cr + method \tab character \tab moment \tab moment, mle, mve, t \tab - \cr nu \tab integer \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - predict.method \tab character \tab plug-in \tab plug-in, predictive, debiased \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - predict.prior \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - prior \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + predict.method \tab character \tab plug-in \tab plug-in, predictive, debiased \tab - \cr + predict.prior \tab list \tab - \tab \tab - \cr + prior \tab list \tab - \tab \tab - \cr tol \tab numeric \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr } } diff --git a/man/mlr_learners_classif.log_reg.Rd b/man/mlr_learners_classif.log_reg.Rd index 95a83cc6..9bc51946 100644 --- a/man/mlr_learners_classif.log_reg.Rd +++ b/man/mlr_learners_classif.log_reg.Rd @@ -47,18 +47,18 @@ lrn("classif.log_reg") \section{Parameters}{ \tabular{lllll}{ Id \tab Type \tab Default \tab Levels \tab Range \cr - dispersion \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + dispersion \tab list \tab NULL \tab \tab - \cr epsilon \tab numeric \tab 1e-08 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - etastart \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + etastart \tab list \tab - \tab \tab - \cr maxit \tab numeric \tab 25 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - model \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - mustart \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - offset \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - singular.ok \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - start \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - trace \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - x \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - y \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + model \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + mustart \tab list \tab - \tab \tab - \cr + offset \tab list \tab - \tab \tab - \cr + singular.ok \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + start \tab list \tab NULL \tab \tab - \cr + trace \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + x \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + y \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr } } diff --git a/man/mlr_learners_classif.multinom.Rd b/man/mlr_learners_classif.multinom.Rd index b468d5f7..1bbe0144 100644 --- a/man/mlr_learners_classif.multinom.Rd +++ b/man/mlr_learners_classif.multinom.Rd @@ -28,24 +28,24 @@ lrn("classif.multinom") \section{Parameters}{ \tabular{lllll}{ Id \tab Type \tab Default \tab Levels \tab Range \cr - Hess \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + Hess \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr abstol \tab numeric \tab 1e-04 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - censored \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + censored \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr decay \tab numeric \tab 0 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - entropy \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - mask \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + entropy \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + mask \tab list \tab - \tab \tab - \cr maxit \tab integer \tab 100 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr MaxNWts \tab integer \tab 1000 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr - model \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - linout \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + model \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + linout \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr rang \tab numeric \tab 0.7 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr reltol \tab numeric \tab 1e-08 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr size \tab integer \tab - \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr - skip \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - softmax \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - summ \tab character \tab 0 \tab 0, 1, 2, 3 \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - trace \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - Wts \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + skip \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + softmax \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + summ \tab character \tab 0 \tab 0, 1, 2, 3 \tab - \cr + trace \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + Wts \tab list \tab - \tab \tab - \cr } } diff --git a/man/mlr_learners_classif.nnet.Rd b/man/mlr_learners_classif.nnet.Rd index 59c01f5c..5d3b09f6 100644 --- a/man/mlr_learners_classif.nnet.Rd +++ b/man/mlr_learners_classif.nnet.Rd @@ -31,22 +31,22 @@ lrn("classif.nnet") \section{Parameters}{ \tabular{lllll}{ Id \tab Type \tab Default \tab Levels \tab Range \cr - Hess \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + Hess \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr MaxNWts \tab integer \tab 1000 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr - Wts \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + Wts \tab list \tab - \tab \tab - \cr abstol \tab numeric \tab 1e-04 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - censored \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - contrasts \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + censored \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + contrasts \tab list \tab NULL \tab \tab - \cr decay \tab numeric \tab 0 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - mask \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + mask \tab list \tab - \tab \tab - \cr maxit \tab integer \tab 100 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr - na.action \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + na.action \tab list \tab - \tab \tab - \cr rang \tab numeric \tab 0.7 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr reltol \tab numeric \tab 1e-08 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr size \tab integer \tab 3 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - skip \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - subset \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - trace \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + skip \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + subset \tab list \tab - \tab \tab - \cr + trace \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr } } diff --git a/man/mlr_learners_classif.qda.Rd b/man/mlr_learners_classif.qda.Rd index e8e9e89f..97eb8dfd 100644 --- a/man/mlr_learners_classif.qda.Rd +++ b/man/mlr_learners_classif.qda.Rd @@ -34,11 +34,11 @@ lrn("classif.qda") \section{Parameters}{ \tabular{lllll}{ Id \tab Type \tab Default \tab Levels \tab Range \cr - method \tab character \tab moment \tab moment, mle, mve, t \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + method \tab character \tab moment \tab moment, mle, mve, t \tab - \cr nu \tab integer \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - predict.method \tab character \tab plug-in \tab plug-in, predictive, debiased \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - predict.prior \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - prior \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + predict.method \tab character \tab plug-in \tab plug-in, predictive, debiased \tab - \cr + predict.prior \tab list \tab - \tab \tab - \cr + prior \tab list \tab - \tab \tab - \cr } } diff --git a/man/mlr_learners_classif.ranger.Rd b/man/mlr_learners_classif.ranger.Rd index 2e8f8729..86a193c1 100644 --- a/man/mlr_learners_classif.ranger.Rd +++ b/man/mlr_learners_classif.ranger.Rd @@ -47,11 +47,11 @@ lrn("classif.ranger") \tabular{lllll}{ Id \tab Type \tab Default \tab Levels \tab Range \cr alpha \tab numeric \tab 0.5 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - always.split.variables \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - class.weights \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - holdout \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - importance \tab character \tab - \tab none, impurity, impurity_corrected, permutation \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - keep.inbag \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + always.split.variables \tab list \tab - \tab \tab - \cr + class.weights \tab list \tab NULL \tab \tab - \cr + holdout \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + importance \tab character \tab - \tab none, impurity, impurity_corrected, permutation \tab - \cr + keep.inbag \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr max.depth \tab integer \tab NULL \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr min.node.size \tab integer \tab 1 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr min.prop \tab numeric \tab 0.1 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr @@ -61,20 +61,20 @@ lrn("classif.ranger") num.random.splits \tab integer \tab 1 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr num.threads \tab integer \tab 1 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr num.trees \tab integer \tab 500 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr - oob.error \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - regularization.factor \tab list \tab 1 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - regularization.usedepth \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - replace \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - respect.unordered.factors \tab character \tab ignore \tab ignore, order, partition \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + oob.error \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + regularization.factor \tab list \tab 1 \tab \tab - \cr + regularization.usedepth \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + replace \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + respect.unordered.factors \tab character \tab ignore \tab ignore, order, partition \tab - \cr sample.fraction \tab numeric \tab - \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - save.memory \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - scale.permutation.importance \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - se.method \tab character \tab infjack \tab jack, infjack \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + save.memory \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + scale.permutation.importance \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + se.method \tab character \tab infjack \tab jack, infjack \tab - \cr seed \tab integer \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - split.select.weights \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - splitrule \tab character \tab gini \tab gini, extratrees \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - verbose \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - write.forest \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + split.select.weights \tab list \tab NULL \tab \tab - \cr + splitrule \tab character \tab gini \tab gini, extratrees \tab - \cr + verbose \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + write.forest \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr } } diff --git a/man/mlr_learners_classif.svm.Rd b/man/mlr_learners_classif.svm.Rd index 98667f92..1dba478f 100644 --- a/man/mlr_learners_classif.svm.Rd +++ b/man/mlr_learners_classif.svm.Rd @@ -29,21 +29,21 @@ lrn("classif.svm") \tabular{lllll}{ Id \tab Type \tab Default \tab Levels \tab Range \cr cachesize \tab numeric \tab 40 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - class.weights \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + class.weights \tab list \tab NULL \tab \tab - \cr coef0 \tab numeric \tab 0 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr cost \tab numeric \tab 1 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr cross \tab integer \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - decision.values \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + decision.values \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr degree \tab integer \tab 3 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr epsilon \tab numeric \tab - \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - fitted \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + fitted \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr gamma \tab numeric \tab - \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - kernel \tab character \tab radial \tab linear, polynomial, radial, sigmoid \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + kernel \tab character \tab radial \tab linear, polynomial, radial, sigmoid \tab - \cr nu \tab numeric \tab 0.5 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - scale \tab list \tab TRUE \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - shrinking \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + scale \tab list \tab TRUE \tab \tab - \cr + shrinking \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr tolerance \tab numeric \tab 0.001 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - type \tab character \tab C-classification \tab C-classification, nu-classification \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + type \tab character \tab C-classification \tab C-classification, nu-classification \tab - \cr } } diff --git a/man/mlr_learners_classif.xgboost.Rd b/man/mlr_learners_classif.xgboost.Rd index a08baecd..b46e246b 100644 --- a/man/mlr_learners_classif.xgboost.Rd +++ b/man/mlr_learners_classif.xgboost.Rd @@ -55,67 +55,67 @@ lrn("classif.xgboost") \tabular{lllll}{ Id \tab Type \tab Default \tab Levels \tab Range \cr alpha \tab numeric \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - approxcontrib \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + approxcontrib \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr base_score \tab numeric \tab 0.5 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - booster \tab character \tab gbtree \tab gbtree, gblinear, dart \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - callbacks \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + booster \tab character \tab gbtree \tab gbtree, gblinear, dart \tab - \cr + callbacks \tab list \tab NULL \tab \tab - \cr colsample_bylevel \tab numeric \tab 1 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr colsample_bynode \tab numeric \tab 1 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr colsample_bytree \tab numeric \tab 1 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - disable_default_eval_metric \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + disable_default_eval_metric \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr early_stopping_rounds \tab integer \tab NULL \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr eta \tab numeric \tab 0.3 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - eval_metric \tab list \tab error \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - feature_selector \tab character \tab cyclic \tab cyclic, shuffle, random, greedy, thrifty \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - feval \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + eval_metric \tab list \tab error \tab \tab - \cr + feature_selector \tab character \tab cyclic \tab cyclic, shuffle, random, greedy, thrifty \tab - \cr + feval \tab list \tab NULL \tab \tab - \cr gamma \tab numeric \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - grow_policy \tab character \tab depthwise \tab depthwise, lossguide \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - interaction_constraints \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + grow_policy \tab character \tab depthwise \tab depthwise, lossguide \tab - \cr + interaction_constraints \tab list \tab - \tab \tab - \cr lambda \tab numeric \tab 1 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr lambda_bias \tab numeric \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr max_bin \tab integer \tab 256 \tab \tab \eqn{[2, \infty)}{[2, Inf)} \cr max_delta_step \tab numeric \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr max_depth \tab integer \tab 6 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr max_leaves \tab integer \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - maximize \tab logical \tab NULL \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + maximize \tab logical \tab NULL \tab TRUE, FALSE \tab - \cr min_child_weight \tab numeric \tab 1 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr missing \tab numeric \tab NA \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - monotone_constraints \tab list \tab 0 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - normalize_type \tab character \tab tree \tab tree, forest \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + monotone_constraints \tab list \tab 0 \tab \tab - \cr + normalize_type \tab character \tab tree \tab tree, forest \tab - \cr nrounds \tab integer \tab - \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr nthread \tab integer \tab 1 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr ntreelimit \tab integer \tab NULL \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr num_parallel_tree \tab integer \tab 1 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr - objective \tab list \tab binary:logistic \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - one_drop \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - outputmargin \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - predcontrib \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - predictor \tab character \tab cpu_predictor \tab cpu_predictor, gpu_predictor \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - predinteraction \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - predleaf \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + objective \tab list \tab binary:logistic \tab \tab - \cr + one_drop \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + outputmargin \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + predcontrib \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + predictor \tab character \tab cpu_predictor \tab cpu_predictor, gpu_predictor \tab - \cr + predinteraction \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + predleaf \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr print_every_n \tab integer \tab 1 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr - process_type \tab character \tab default \tab default, update \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + process_type \tab character \tab default \tab default, update \tab - \cr rate_drop \tab numeric \tab 0 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - refresh_leaf \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - reshape \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - seed_per_iteration \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - sampling_method \tab character \tab uniform \tab uniform, gradient_based \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - sample_type \tab character \tab uniform \tab uniform, weighted \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - save_name \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + refresh_leaf \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + reshape \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + seed_per_iteration \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + sampling_method \tab character \tab uniform \tab uniform, gradient_based \tab - \cr + sample_type \tab character \tab uniform \tab uniform, weighted \tab - \cr + save_name \tab list \tab NULL \tab \tab - \cr save_period \tab integer \tab NULL \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr scale_pos_weight \tab numeric \tab 1 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr sketch_eps \tab numeric \tab 0.03 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr skip_drop \tab numeric \tab 0 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - single_precision_histogram \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + single_precision_histogram \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr subsample \tab numeric \tab 1 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr top_k \tab integer \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - training \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - tree_method \tab character \tab auto \tab auto, exact, approx, hist, gpu_hist \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + training \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + tree_method \tab character \tab auto \tab auto, exact, approx, hist, gpu_hist \tab - \cr tweedie_variance_power \tab numeric \tab 1.5 \tab \tab \eqn{[1, 2]}{[1, 2]} \cr - updater \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + updater \tab list \tab - \tab \tab - \cr verbose \tab integer \tab 1 \tab \tab \eqn{[0, 2]}{[0, 2]} \cr - watchlist \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - xgb_model \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + watchlist \tab list \tab NULL \tab \tab - \cr + xgb_model \tab list \tab NULL \tab \tab - \cr } } diff --git a/man/mlr_learners_regr.cv_glmnet.Rd b/man/mlr_learners_regr.cv_glmnet.Rd index 341acdb9..c06add60 100644 --- a/man/mlr_learners_regr.cv_glmnet.Rd +++ b/man/mlr_learners_regr.cv_glmnet.Rd @@ -30,7 +30,7 @@ lrn("regr.cv_glmnet") \section{Parameters}{ \tabular{lllll}{ Id \tab Type \tab Default \tab Levels \tab Range \cr - alignment \tab character \tab lambda \tab lambda, fraction \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + alignment \tab character \tab lambda \tab lambda, fraction \tab - \cr alpha \tab numeric \tab 1 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr big \tab numeric \tab 9.9e+35 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr devmax \tab numeric \tab 0.999 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr @@ -39,40 +39,40 @@ lrn("regr.cv_glmnet") epsnr \tab numeric \tab 1e-08 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr exclude \tab integer \tab - \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr exmx \tab numeric \tab 250 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - family \tab character \tab gaussian \tab gaussian, poisson \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + family \tab character \tab gaussian \tab gaussian, poisson \tab - \cr fdev \tab numeric \tab 1e-05 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - foldid \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - gamma \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - grouped \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - intercept \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - keep \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - lambda \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + foldid \tab list \tab NULL \tab \tab - \cr + gamma \tab list \tab - \tab \tab - \cr + grouped \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + intercept \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + keep \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + lambda \tab list \tab - \tab \tab - \cr lambda.min.ratio \tab numeric \tab - \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - lower.limits \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + lower.limits \tab list \tab - \tab \tab - \cr maxit \tab integer \tab 100000 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr mnlam \tab integer \tab 5 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr mxit \tab integer \tab 100 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr mxitnr \tab integer \tab 25 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr nfolds \tab integer \tab 10 \tab \tab \eqn{[3, \infty)}{[3, Inf)} \cr nlambda \tab integer \tab 100 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr - offset \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - parallel \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - penalty.factor \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + offset \tab list \tab NULL \tab \tab - \cr + parallel \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + penalty.factor \tab list \tab - \tab \tab - \cr pmax \tab integer \tab - \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr pmin \tab numeric \tab 1e-09 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr prec \tab numeric \tab 1e-10 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr predict.gamma \tab numeric \tab gamma.1se \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - relax \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + relax \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr s \tab numeric \tab lambda.1se \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - standardize \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - standardize.response \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + standardize \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + standardize.response \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr thresh \tab numeric \tab 1e-07 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr trace.it \tab integer \tab 0 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - type.gaussian \tab character \tab - \tab covariance, naive \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - type.logistic \tab character \tab - \tab Newton, modified.Newton \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - type.measure \tab character \tab deviance \tab deviance, class, auc, mse, mae \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - type.multinomial \tab character \tab - \tab ungrouped, grouped \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - upper.limits \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + type.gaussian \tab character \tab - \tab covariance, naive \tab - \cr + type.logistic \tab character \tab - \tab Newton, modified.Newton \tab - \cr + type.measure \tab character \tab deviance \tab deviance, class, auc, mse, mae \tab - \cr + type.multinomial \tab character \tab - \tab ungrouped, grouped \tab - \cr + upper.limits \tab list \tab - \tab \tab - \cr } } diff --git a/man/mlr_learners_regr.glmnet.Rd b/man/mlr_learners_regr.glmnet.Rd index d79d5916..a535bd55 100644 --- a/man/mlr_learners_regr.glmnet.Rd +++ b/man/mlr_learners_regr.glmnet.Rd @@ -48,47 +48,47 @@ lrn("regr.glmnet") \section{Parameters}{ \tabular{lllll}{ Id \tab Type \tab Default \tab Levels \tab Range \cr - alignment \tab character \tab lambda \tab lambda, fraction \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + alignment \tab character \tab lambda \tab lambda, fraction \tab - \cr alpha \tab numeric \tab 1 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr big \tab numeric \tab 9.9e+35 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr devmax \tab numeric \tab 0.999 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr dfmax \tab integer \tab - \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr eps \tab numeric \tab 1e-06 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr epsnr \tab numeric \tab 1e-08 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - exact \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + exact \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr exclude \tab integer \tab - \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr exmx \tab numeric \tab 250 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - family \tab character \tab gaussian \tab gaussian, poisson \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + family \tab character \tab gaussian \tab gaussian, poisson \tab - \cr fdev \tab numeric \tab 1e-05 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr gamma \tab numeric \tab 1 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - grouped \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - intercept \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - keep \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - lambda \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + grouped \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + intercept \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + keep \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + lambda \tab list \tab - \tab \tab - \cr lambda.min.ratio \tab numeric \tab - \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - lower.limits \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + lower.limits \tab list \tab - \tab \tab - \cr maxit \tab integer \tab 100000 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr mnlam \tab integer \tab 5 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr mxit \tab integer \tab 100 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr mxitnr \tab integer \tab 25 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr - newoffset \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + newoffset \tab list \tab - \tab \tab - \cr nlambda \tab integer \tab 100 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr - offset \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - parallel \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - penalty.factor \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + offset \tab list \tab NULL \tab \tab - \cr + parallel \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + penalty.factor \tab list \tab - \tab \tab - \cr pmax \tab integer \tab - \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr pmin \tab numeric \tab 1e-09 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr prec \tab numeric \tab 1e-10 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - relax \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + relax \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr s \tab numeric \tab 0.01 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - standardize \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - standardize.response \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + standardize \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + standardize.response \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr thresh \tab numeric \tab 1e-07 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr trace.it \tab integer \tab 0 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - type.gaussian \tab character \tab - \tab covariance, naive \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - type.logistic \tab character \tab - \tab Newton, modified.Newton \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - type.multinomial \tab character \tab - \tab ungrouped, grouped \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - upper.limits \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + type.gaussian \tab character \tab - \tab covariance, naive \tab - \cr + type.logistic \tab character \tab - \tab Newton, modified.Newton \tab - \cr + type.multinomial \tab character \tab - \tab ungrouped, grouped \tab - \cr + upper.limits \tab list \tab - \tab \tab - \cr } } diff --git a/man/mlr_learners_regr.kknn.Rd b/man/mlr_learners_regr.kknn.Rd index d22a5a67..4398ce0b 100644 --- a/man/mlr_learners_regr.kknn.Rd +++ b/man/mlr_learners_regr.kknn.Rd @@ -41,9 +41,9 @@ lrn("regr.kknn") Id \tab Type \tab Default \tab Levels \tab Range \cr k \tab integer \tab 7 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr distance \tab numeric \tab 2 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - kernel \tab character \tab optimal \tab rectangular, triangular, epanechnikov, biweight, triweight, cos, inv, gaussian, rank, optimal \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - scale \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - ykernel \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + kernel \tab character \tab optimal \tab rectangular, triangular, epanechnikov, biweight, triweight, cos, inv, gaussian, rank, optimal \tab - \cr + scale \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + ykernel \tab list \tab NULL \tab \tab - \cr } } diff --git a/man/mlr_learners_regr.km.Rd b/man/mlr_learners_regr.km.Rd index 4e1dd23e..73821e1f 100644 --- a/man/mlr_learners_regr.km.Rd +++ b/man/mlr_learners_regr.km.Rd @@ -37,34 +37,34 @@ lrn("regr.km") \section{Parameters}{ \tabular{lllll}{ Id \tab Type \tab Default \tab Levels \tab Range \cr - bias.correct \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - checkNames \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - coef.cov \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - coef.trend \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - coef.var \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - control \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - cov.compute \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - covtype \tab character \tab matern5_2 \tab gauss, matern5_2, matern3_2, exp, powexp \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - estim.method \tab character \tab MLE \tab MLE, LOO \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - gr \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - iso \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + bias.correct \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + checkNames \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + coef.cov \tab list \tab NULL \tab \tab - \cr + coef.trend \tab list \tab NULL \tab \tab - \cr + coef.var \tab list \tab NULL \tab \tab - \cr + control \tab list \tab NULL \tab \tab - \cr + cov.compute \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + covtype \tab character \tab matern5_2 \tab gauss, matern5_2, matern3_2, exp, powexp \tab - \cr + estim.method \tab character \tab MLE \tab MLE, LOO \tab - \cr + gr \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + iso \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr jitter \tab numeric \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - kernel \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - knots \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - light.return \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - lower \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + kernel \tab list \tab NULL \tab \tab - \cr + knots \tab list \tab NULL \tab \tab - \cr + light.return \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + lower \tab list \tab NULL \tab \tab - \cr multistart \tab integer \tab 1 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - noise.var \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + noise.var \tab list \tab NULL \tab \tab - \cr nugget \tab numeric \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - nugget.estim \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + nugget.estim \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr nugget.stability \tab numeric \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - optim.method \tab character \tab BFGS \tab BFGS, gen \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - parinit \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - penalty \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - scaling \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - se.compute \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - type \tab character \tab SK \tab SK, UK \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - upper \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + optim.method \tab character \tab BFGS \tab BFGS, gen \tab - \cr + parinit \tab list \tab NULL \tab \tab - \cr + penalty \tab list \tab NULL \tab \tab - \cr + scaling \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + se.compute \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + type \tab character \tab SK \tab SK, UK \tab - \cr + upper \tab list \tab NULL \tab \tab - \cr } } diff --git a/man/mlr_learners_regr.lm.Rd b/man/mlr_learners_regr.lm.Rd index 9b1ce62d..6ec3130c 100644 --- a/man/mlr_learners_regr.lm.Rd +++ b/man/mlr_learners_regr.lm.Rd @@ -29,16 +29,16 @@ lrn("regr.lm") \tabular{lllll}{ Id \tab Type \tab Default \tab Levels \tab Range \cr df \tab numeric \tab Inf \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - interval \tab character \tab - \tab none, confidence, prediction \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + interval \tab character \tab - \tab none, confidence, prediction \tab - \cr level \tab numeric \tab 0.95 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - model \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - offset \tab logical \tab - \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - pred.var \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - qr \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + model \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + offset \tab logical \tab - \tab TRUE, FALSE \tab - \cr + pred.var \tab list \tab - \tab \tab - \cr + qr \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr scale \tab numeric \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - singular.ok \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - x \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - y \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + singular.ok \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + x \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + y \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr } } diff --git a/man/mlr_learners_regr.ranger.Rd b/man/mlr_learners_regr.ranger.Rd index eb4c7e05..21445f4b 100644 --- a/man/mlr_learners_regr.ranger.Rd +++ b/man/mlr_learners_regr.ranger.Rd @@ -29,10 +29,10 @@ lrn("regr.ranger") \tabular{lllll}{ Id \tab Type \tab Default \tab Levels \tab Range \cr alpha \tab numeric \tab 0.5 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - always.split.variables \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - holdout \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - importance \tab character \tab - \tab none, impurity, impurity_corrected, permutation \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - keep.inbag \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + always.split.variables \tab list \tab - \tab \tab - \cr + holdout \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + importance \tab character \tab - \tab none, impurity, impurity_corrected, permutation \tab - \cr + keep.inbag \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr max.depth \tab integer \tab NULL \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr min.node.size \tab integer \tab 5 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr min.prop \tab numeric \tab 0.1 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr @@ -42,21 +42,21 @@ lrn("regr.ranger") num.random.splits \tab integer \tab 1 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr num.threads \tab integer \tab 1 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr num.trees \tab integer \tab 500 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr - oob.error \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - quantreg \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - regularization.factor \tab list \tab 1 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - regularization.usedepth \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - replace \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - respect.unordered.factors \tab character \tab ignore \tab ignore, order, partition \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + oob.error \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + quantreg \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + regularization.factor \tab list \tab 1 \tab \tab - \cr + regularization.usedepth \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + replace \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + respect.unordered.factors \tab character \tab ignore \tab ignore, order, partition \tab - \cr sample.fraction \tab numeric \tab - \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - save.memory \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - scale.permutation.importance \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - se.method \tab character \tab infjack \tab jack, infjack \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + save.memory \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + scale.permutation.importance \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + se.method \tab character \tab infjack \tab jack, infjack \tab - \cr seed \tab integer \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - split.select.weights \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - splitrule \tab character \tab variance \tab variance, extratrees, maxstat \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - verbose \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - write.forest \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + split.select.weights \tab list \tab NULL \tab \tab - \cr + splitrule \tab character \tab variance \tab variance, extratrees, maxstat \tab - \cr + verbose \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + write.forest \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr } } diff --git a/man/mlr_learners_regr.svm.Rd b/man/mlr_learners_regr.svm.Rd index 1e7a42f2..6ea606a6 100644 --- a/man/mlr_learners_regr.svm.Rd +++ b/man/mlr_learners_regr.svm.Rd @@ -34,14 +34,14 @@ lrn("regr.svm") cross \tab integer \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr degree \tab integer \tab 3 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr epsilon \tab numeric \tab - \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - fitted \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + fitted \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr gamma \tab numeric \tab - \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - kernel \tab character \tab radial \tab linear, polynomial, radial, sigmoid \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + kernel \tab character \tab radial \tab linear, polynomial, radial, sigmoid \tab - \cr nu \tab numeric \tab 0.5 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - scale \tab list \tab TRUE \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - shrinking \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + scale \tab list \tab TRUE \tab \tab - \cr + shrinking \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr tolerance \tab numeric \tab 0.001 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - type \tab character \tab eps-regression \tab eps-regression, nu-regression \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + type \tab character \tab eps-regression \tab eps-regression, nu-regression \tab - \cr } } diff --git a/man/mlr_learners_regr.xgboost.Rd b/man/mlr_learners_regr.xgboost.Rd index 0cec28ea..f0631b9e 100644 --- a/man/mlr_learners_regr.xgboost.Rd +++ b/man/mlr_learners_regr.xgboost.Rd @@ -29,67 +29,67 @@ lrn("regr.xgboost") \tabular{lllll}{ Id \tab Type \tab Default \tab Levels \tab Range \cr alpha \tab numeric \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - approxcontrib \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + approxcontrib \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr base_score \tab numeric \tab 0.5 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - booster \tab character \tab gbtree \tab gbtree, gblinear, dart \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - callbacks \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + booster \tab character \tab gbtree \tab gbtree, gblinear, dart \tab - \cr + callbacks \tab list \tab NULL \tab \tab - \cr colsample_bylevel \tab numeric \tab 1 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr colsample_bynode \tab numeric \tab 1 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr colsample_bytree \tab numeric \tab 1 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - disable_default_eval_metric \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + disable_default_eval_metric \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr early_stopping_rounds \tab integer \tab NULL \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr eta \tab numeric \tab 0.3 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - eval_metric \tab list \tab rmse \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - feature_selector \tab character \tab cyclic \tab cyclic, shuffle, random, greedy, thrifty \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - feval \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + eval_metric \tab list \tab rmse \tab \tab - \cr + feature_selector \tab character \tab cyclic \tab cyclic, shuffle, random, greedy, thrifty \tab - \cr + feval \tab list \tab NULL \tab \tab - \cr gamma \tab numeric \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - grow_policy \tab character \tab depthwise \tab depthwise, lossguide \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - interaction_constraints \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + grow_policy \tab character \tab depthwise \tab depthwise, lossguide \tab - \cr + interaction_constraints \tab list \tab - \tab \tab - \cr lambda \tab numeric \tab 1 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr lambda_bias \tab numeric \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr max_bin \tab integer \tab 256 \tab \tab \eqn{[2, \infty)}{[2, Inf)} \cr max_delta_step \tab numeric \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr max_depth \tab integer \tab 6 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr max_leaves \tab integer \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - maximize \tab logical \tab NULL \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + maximize \tab logical \tab NULL \tab TRUE, FALSE \tab - \cr min_child_weight \tab numeric \tab 1 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr missing \tab numeric \tab NA \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - monotone_constraints \tab list \tab 0 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - normalize_type \tab character \tab tree \tab tree, forest \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + monotone_constraints \tab list \tab 0 \tab \tab - \cr + normalize_type \tab character \tab tree \tab tree, forest \tab - \cr nrounds \tab integer \tab - \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr nthread \tab integer \tab 1 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr ntreelimit \tab integer \tab NULL \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr num_parallel_tree \tab integer \tab 1 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr - objective \tab list \tab reg:squarederror \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - one_drop \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - outputmargin \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - predcontrib \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - predictor \tab character \tab cpu_predictor \tab cpu_predictor, gpu_predictor \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - predinteraction \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - predleaf \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + objective \tab list \tab reg:squarederror \tab \tab - \cr + one_drop \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + outputmargin \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + predcontrib \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + predictor \tab character \tab cpu_predictor \tab cpu_predictor, gpu_predictor \tab - \cr + predinteraction \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + predleaf \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr print_every_n \tab integer \tab 1 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr - process_type \tab character \tab default \tab default, update \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + process_type \tab character \tab default \tab default, update \tab - \cr rate_drop \tab numeric \tab 0 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - refresh_leaf \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - reshape \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - sampling_method \tab character \tab uniform \tab uniform, gradient_based \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - sample_type \tab character \tab uniform \tab uniform, weighted \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - save_name \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + refresh_leaf \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + reshape \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + sampling_method \tab character \tab uniform \tab uniform, gradient_based \tab - \cr + sample_type \tab character \tab uniform \tab uniform, weighted \tab - \cr + save_name \tab list \tab NULL \tab \tab - \cr save_period \tab integer \tab NULL \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr scale_pos_weight \tab numeric \tab 1 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - seed_per_iteration \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + seed_per_iteration \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr sketch_eps \tab numeric \tab 0.03 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr skip_drop \tab numeric \tab 0 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - single_precision_histogram \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + single_precision_histogram \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr subsample \tab numeric \tab 1 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr top_k \tab integer \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - training \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - tree_method \tab character \tab auto \tab auto, exact, approx, hist, gpu_hist \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + training \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + tree_method \tab character \tab auto \tab auto, exact, approx, hist, gpu_hist \tab - \cr tweedie_variance_power \tab numeric \tab 1.5 \tab \tab \eqn{[1, 2]}{[1, 2]} \cr - updater \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + updater \tab list \tab - \tab \tab - \cr verbose \tab integer \tab 1 \tab \tab \eqn{[0, 2]}{[0, 2]} \cr - watchlist \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - xgb_model \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + watchlist \tab list \tab NULL \tab \tab - \cr + xgb_model \tab list \tab NULL \tab \tab - \cr } } diff --git a/man/mlr_learners_surv.cv_glmnet.Rd b/man/mlr_learners_surv.cv_glmnet.Rd index 49df0c14..d7976bd7 100644 --- a/man/mlr_learners_surv.cv_glmnet.Rd +++ b/man/mlr_learners_surv.cv_glmnet.Rd @@ -30,48 +30,48 @@ lrn("surv.cv_glmnet") \section{Parameters}{ \tabular{lllll}{ Id \tab Type \tab Default \tab Levels \tab Range \cr - alignment \tab character \tab lambda \tab lambda, fraction \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + alignment \tab character \tab lambda \tab lambda, fraction \tab - \cr alpha \tab numeric \tab 1 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr big \tab numeric \tab 9.9e+35 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr devmax \tab numeric \tab 0.999 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr dfmax \tab integer \tab - \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr eps \tab numeric \tab 1e-06 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr epsnr \tab numeric \tab 1e-08 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - exclude \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + exclude \tab list \tab - \tab \tab - \cr exmx \tab numeric \tab 250 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr fdev \tab numeric \tab 1e-05 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - foldid \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - gamma \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - grouped \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - intercept \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - keep \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - lambda \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + foldid \tab list \tab NULL \tab \tab - \cr + gamma \tab list \tab - \tab \tab - \cr + grouped \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + intercept \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + keep \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + lambda \tab list \tab - \tab \tab - \cr lambda.min.ratio \tab numeric \tab - \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - lower.limits \tab list \tab -Inf \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + lower.limits \tab list \tab -Inf \tab \tab - \cr maxit \tab integer \tab 100000 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr mnlam \tab integer \tab 5 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr mxit \tab integer \tab 100 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr mxitnr \tab integer \tab 25 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr nfolds \tab integer \tab 10 \tab \tab \eqn{[3, \infty)}{[3, Inf)} \cr nlambda \tab integer \tab 100 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr - offset \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - parallel \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - penalty.factor \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + offset \tab list \tab NULL \tab \tab - \cr + parallel \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + penalty.factor \tab list \tab - \tab \tab - \cr pmax \tab integer \tab - \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr pmin \tab numeric \tab 1e-09 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr prec \tab numeric \tab 1e-10 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr predict.gamma \tab numeric \tab gamma.1se \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - relax \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + relax \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr s \tab numeric \tab lambda.1se \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - standardize \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - standardize.response \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + standardize \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + standardize.response \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr thresh \tab numeric \tab 1e-07 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr trace.it \tab integer \tab 0 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - type.gaussian \tab character \tab - \tab covariance, naive \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - type.logistic \tab character \tab Newton \tab Newton, modified.Newton \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - type.measure \tab character \tab deviance \tab deviance, C \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - type.multinomial \tab character \tab ungrouped \tab ungrouped, grouped \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - upper.limits \tab list \tab Inf \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + type.gaussian \tab character \tab - \tab covariance, naive \tab - \cr + type.logistic \tab character \tab Newton \tab Newton, modified.Newton \tab - \cr + type.measure \tab character \tab deviance \tab deviance, C \tab - \cr + type.multinomial \tab character \tab ungrouped \tab ungrouped, grouped \tab - \cr + upper.limits \tab list \tab Inf \tab \tab - \cr } } diff --git a/man/mlr_learners_surv.glmnet.Rd b/man/mlr_learners_surv.glmnet.Rd index 0c117b85..e7030d8c 100644 --- a/man/mlr_learners_surv.glmnet.Rd +++ b/man/mlr_learners_surv.glmnet.Rd @@ -48,45 +48,45 @@ lrn("surv.glmnet") \section{Parameters}{ \tabular{lllll}{ Id \tab Type \tab Default \tab Levels \tab Range \cr - alignment \tab character \tab lambda \tab lambda, fraction \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + alignment \tab character \tab lambda \tab lambda, fraction \tab - \cr alpha \tab numeric \tab 1 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr big \tab numeric \tab 9.9e+35 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr devmax \tab numeric \tab 0.999 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr dfmax \tab integer \tab - \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr eps \tab numeric \tab 1e-06 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr epsnr \tab numeric \tab 1e-08 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - exact \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - exclude \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + exact \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + exclude \tab list \tab - \tab \tab - \cr exmx \tab numeric \tab 250 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr fdev \tab numeric \tab 1e-05 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - gamma \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - grouped \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - intercept \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - keep \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - lambda \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + gamma \tab list \tab - \tab \tab - \cr + grouped \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + intercept \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + keep \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + lambda \tab list \tab - \tab \tab - \cr lambda.min.ratio \tab numeric \tab - \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - lower.limits \tab list \tab -Inf \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + lower.limits \tab list \tab -Inf \tab \tab - \cr maxit \tab integer \tab 100000 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr mnlam \tab integer \tab 5 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr mxit \tab integer \tab 100 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr mxitnr \tab integer \tab 25 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr - newoffset \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + newoffset \tab list \tab - \tab \tab - \cr nlambda \tab integer \tab 100 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr - offset \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - parallel \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - penalty.factor \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + offset \tab list \tab NULL \tab \tab - \cr + parallel \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + penalty.factor \tab list \tab - \tab \tab - \cr pmax \tab integer \tab - \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr pmin \tab numeric \tab 1e-09 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr prec \tab numeric \tab 1e-10 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr predict.gamma \tab numeric \tab gamma.1se \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - relax \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + relax \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr s \tab numeric \tab 0.01 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - standardize \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + standardize \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr thresh \tab numeric \tab 1e-07 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr trace.it \tab integer \tab 0 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - type.logistic \tab character \tab Newton \tab Newton, modified.Newton \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - type.multinomial \tab character \tab ungrouped \tab ungrouped, grouped \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - upper.limits \tab list \tab Inf \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + type.logistic \tab character \tab Newton \tab Newton, modified.Newton \tab - \cr + type.multinomial \tab character \tab ungrouped \tab ungrouped, grouped \tab - \cr + upper.limits \tab list \tab Inf \tab \tab - \cr } } diff --git a/man/mlr_learners_surv.ranger.Rd b/man/mlr_learners_surv.ranger.Rd index bff9ce38..d04d4d11 100644 --- a/man/mlr_learners_surv.ranger.Rd +++ b/man/mlr_learners_surv.ranger.Rd @@ -29,10 +29,10 @@ lrn("surv.ranger") \tabular{lllll}{ Id \tab Type \tab Default \tab Levels \tab Range \cr alpha \tab numeric \tab 0.5 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - always.split.variables \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - holdout \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - importance \tab character \tab - \tab none, impurity, impurity_corrected, permutation \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - keep.inbag \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + always.split.variables \tab list \tab - \tab \tab - \cr + holdout \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + importance \tab character \tab - \tab none, impurity, impurity_corrected, permutation \tab - \cr + keep.inbag \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr max.depth \tab integer \tab NULL \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr min.node.size \tab integer \tab 5 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr minprop \tab numeric \tab 0.1 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr @@ -41,19 +41,19 @@ lrn("surv.ranger") num.random.splits \tab integer \tab 1 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr num.threads \tab integer \tab 1 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr num.trees \tab integer \tab 500 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr - oob.error \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - regularization.factor \tab list \tab 1 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - regularization.usedepth \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - replace \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - respect.unordered.factors \tab character \tab ignore \tab ignore, order, partition \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + oob.error \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + regularization.factor \tab list \tab 1 \tab \tab - \cr + regularization.usedepth \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + replace \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + respect.unordered.factors \tab character \tab ignore \tab ignore, order, partition \tab - \cr sample.fraction \tab numeric \tab - \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - save.memory \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - scale.permutation.importance \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + save.memory \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + scale.permutation.importance \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr seed \tab integer \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr split.select.weights \tab numeric \tab - \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - splitrule \tab character \tab logrank \tab logrank, extratrees, C, maxstat \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - verbose \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - write.forest \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + splitrule \tab character \tab logrank \tab logrank, extratrees, C, maxstat \tab - \cr + verbose \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + write.forest \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr } } diff --git a/man/mlr_learners_surv.xgboost.Rd b/man/mlr_learners_surv.xgboost.Rd index 3461f1b9..b3af4fb9 100644 --- a/man/mlr_learners_surv.xgboost.Rd +++ b/man/mlr_learners_surv.xgboost.Rd @@ -60,62 +60,62 @@ lrn("surv.xgboost") \section{Parameters}{ \tabular{lllll}{ Id \tab Type \tab Default \tab Levels \tab Range \cr - aft_loss_distribution \tab character \tab normal \tab normal, logistic, extreme \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + aft_loss_distribution \tab character \tab normal \tab normal, logistic, extreme \tab - \cr aft_loss_distribution_scale \tab numeric \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr alpha \tab numeric \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr base_score \tab numeric \tab 0.5 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - booster \tab character \tab gbtree \tab gbtree, gblinear, dart \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - callbacks \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + booster \tab character \tab gbtree \tab gbtree, gblinear, dart \tab - \cr + callbacks \tab list \tab NULL \tab \tab - \cr colsample_bylevel \tab numeric \tab 1 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr colsample_bynode \tab numeric \tab 1 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr colsample_bytree \tab numeric \tab 1 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - disable_default_eval_metric \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + disable_default_eval_metric \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr early_stopping_rounds \tab integer \tab NULL \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr eta \tab numeric \tab 0.3 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - feature_selector \tab character \tab cyclic \tab cyclic, shuffle, random, greedy, thrifty \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - feval \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + feature_selector \tab character \tab cyclic \tab cyclic, shuffle, random, greedy, thrifty \tab - \cr + feval \tab list \tab NULL \tab \tab - \cr gamma \tab numeric \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - grow_policy \tab character \tab depthwise \tab depthwise, lossguide \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - interaction_constraints \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + grow_policy \tab character \tab depthwise \tab depthwise, lossguide \tab - \cr + interaction_constraints \tab list \tab - \tab \tab - \cr lambda \tab numeric \tab 1 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr lambda_bias \tab numeric \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr max_bin \tab integer \tab 256 \tab \tab \eqn{[2, \infty)}{[2, Inf)} \cr max_delta_step \tab numeric \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr max_depth \tab integer \tab 6 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr max_leaves \tab integer \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - maximize \tab logical \tab NULL \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + maximize \tab logical \tab NULL \tab TRUE, FALSE \tab - \cr min_child_weight \tab numeric \tab 1 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr missing \tab numeric \tab NA \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr monotone_constraints \tab integer \tab 0 \tab \tab \eqn{[-1, 1]}{[-1, 1]} \cr - normalize_type \tab character \tab tree \tab tree, forest \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + normalize_type \tab character \tab tree \tab tree, forest \tab - \cr nrounds \tab integer \tab - \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr nthread \tab integer \tab 1 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr ntreelimit \tab integer \tab - \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr num_parallel_tree \tab integer \tab 1 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr - objective \tab character \tab survival:cox \tab survival:cox, survival:aft \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - one_drop \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - predictor \tab character \tab cpu_predictor \tab cpu_predictor, gpu_predictor \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + objective \tab character \tab survival:cox \tab survival:cox, survival:aft \tab - \cr + one_drop \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + predictor \tab character \tab cpu_predictor \tab cpu_predictor, gpu_predictor \tab - \cr print_every_n \tab integer \tab 1 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr - process_type \tab character \tab default \tab default, update \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + process_type \tab character \tab default \tab default, update \tab - \cr rate_drop \tab numeric \tab 0 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - refresh_leaf \tab logical \tab TRUE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - sampling_method \tab character \tab uniform \tab uniform, gradient_based \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - sample_type \tab character \tab uniform \tab uniform, weighted \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - save_name \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + refresh_leaf \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr + sampling_method \tab character \tab uniform \tab uniform, gradient_based \tab - \cr + sample_type \tab character \tab uniform \tab uniform, weighted \tab - \cr + save_name \tab list \tab - \tab \tab - \cr save_period \tab integer \tab - \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr scale_pos_weight \tab numeric \tab 1 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - seed_per_iteration \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + seed_per_iteration \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr sketch_eps \tab numeric \tab 0.03 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr skip_drop \tab numeric \tab 0 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - single_precision_histogram \tab logical \tab FALSE \tab TRUE, FALSE \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + single_precision_histogram \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr subsample \tab numeric \tab 1 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr top_k \tab integer \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - tree_method \tab character \tab auto \tab auto, exact, approx, hist, gpu_hist \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + tree_method \tab character \tab auto \tab auto, exact, approx, hist, gpu_hist \tab - \cr tweedie_variance_power \tab numeric \tab 1.5 \tab \tab \eqn{[1, 2]}{[1, 2]} \cr - updater \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + updater \tab list \tab - \tab \tab - \cr verbose \tab integer \tab 1 \tab \tab \eqn{[0, 2]}{[0, 2]} \cr - watchlist \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - xgb_model \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr + watchlist \tab list \tab NULL \tab \tab - \cr + xgb_model \tab list \tab - \tab \tab - \cr } } From ab5b7a05aadf45db71a4a59a9d85977bcef8aa2d Mon Sep 17 00:00:00 2001 From: Michel Lang Date: Thu, 18 Nov 2021 13:57:15 +0100 Subject: [PATCH 4/4] ... --- NAMESPACE | 2 +- R/zzz.R | 3 ++- man/mlr_learners_classif.xgboost.Rd | 10 +++------- man/mlr_learners_regr.xgboost.Rd | 2 ++ man/mlr_learners_surv.xgboost.Rd | 2 ++ 5 files changed, 10 insertions(+), 9 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index b62105c6..dedf95d4 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -34,5 +34,5 @@ importFrom(mlr3,LearnerRegr) importFrom(mlr3,mlr_learners) importFrom(stats,coef) importFrom(stats,predict) -importFrom(stats,tail) importFrom(utils,bibentry) +importFrom(utils,tail) diff --git a/R/zzz.R b/R/zzz.R index 582032a1..7d0ea135 100644 --- a/R/zzz.R +++ b/R/zzz.R @@ -4,7 +4,8 @@ #' @import checkmate #' @importFrom R6 R6Class #' @importFrom mlr3 mlr_learners LearnerClassif LearnerRegr -#' @importFrom stats predict coef tail +#' @importFrom stats predict coef +#' @importFrom utils tail #' #' @description #' More learners are implemented in the [mlr3extralearners package](https://github.com/mlr-org/mlr3extralearners). diff --git a/man/mlr_learners_classif.xgboost.Rd b/man/mlr_learners_classif.xgboost.Rd index 8be150bc..18ce87e3 100644 --- a/man/mlr_learners_classif.xgboost.Rd +++ b/man/mlr_learners_classif.xgboost.Rd @@ -69,18 +69,13 @@ lrn("classif.xgboost") disable_default_eval_metric \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr early_stopping_rounds \tab integer \tab NULL \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr eta \tab numeric \tab 0.3 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr -<<<<<<< HEAD - eval_metric \tab list \tab error \tab \tab - \cr + eval_metric \tab list \tab - \tab \tab - \cr feature_selector \tab character \tab cyclic \tab cyclic, shuffle, random, greedy, thrifty \tab - \cr feval \tab list \tab NULL \tab \tab - \cr -======= - eval_metric \tab list \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - feature_selector \tab character \tab cyclic \tab cyclic, shuffle, random, greedy, thrifty \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - feval \tab list \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr ->>>>>>> main gamma \tab numeric \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr grow_policy \tab character \tab depthwise \tab depthwise, lossguide \tab - \cr interaction_constraints \tab list \tab - \tab \tab - \cr + iterationrange \tab list \tab - \tab \tab - \cr lambda \tab numeric \tab 1 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr lambda_bias \tab numeric \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr max_bin \tab integer \tab 256 \tab \tab \eqn{[2, \infty)}{[2, Inf)} \cr @@ -117,6 +112,7 @@ lrn("classif.xgboost") sketch_eps \tab numeric \tab 0.03 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr skip_drop \tab numeric \tab 0 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr single_precision_histogram \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + strict_shape \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr subsample \tab numeric \tab 1 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr top_k \tab integer \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr training \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr diff --git a/man/mlr_learners_regr.xgboost.Rd b/man/mlr_learners_regr.xgboost.Rd index f0631b9e..5b666b0d 100644 --- a/man/mlr_learners_regr.xgboost.Rd +++ b/man/mlr_learners_regr.xgboost.Rd @@ -45,6 +45,7 @@ lrn("regr.xgboost") gamma \tab numeric \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr grow_policy \tab character \tab depthwise \tab depthwise, lossguide \tab - \cr interaction_constraints \tab list \tab - \tab \tab - \cr + iterationrange \tab list \tab - \tab \tab - \cr lambda \tab numeric \tab 1 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr lambda_bias \tab numeric \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr max_bin \tab integer \tab 256 \tab \tab \eqn{[2, \infty)}{[2, Inf)} \cr @@ -81,6 +82,7 @@ lrn("regr.xgboost") sketch_eps \tab numeric \tab 0.03 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr skip_drop \tab numeric \tab 0 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr single_precision_histogram \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + strict_shape \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr subsample \tab numeric \tab 1 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr top_k \tab integer \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr training \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr diff --git a/man/mlr_learners_surv.xgboost.Rd b/man/mlr_learners_surv.xgboost.Rd index 98f53210..20fbacef 100644 --- a/man/mlr_learners_surv.xgboost.Rd +++ b/man/mlr_learners_surv.xgboost.Rd @@ -77,6 +77,7 @@ lrn("surv.xgboost") gamma \tab numeric \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr grow_policy \tab character \tab depthwise \tab depthwise, lossguide \tab - \cr interaction_constraints \tab list \tab - \tab \tab - \cr + iterationrange \tab list \tab - \tab \tab - \cr lambda \tab numeric \tab 1 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr lambda_bias \tab numeric \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr max_bin \tab integer \tab 256 \tab \tab \eqn{[2, \infty)}{[2, Inf)} \cr @@ -108,6 +109,7 @@ lrn("surv.xgboost") sketch_eps \tab numeric \tab 0.03 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr skip_drop \tab numeric \tab 0 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr single_precision_histogram \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr + strict_shape \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr subsample \tab numeric \tab 1 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr top_k \tab integer \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr tree_method \tab character \tab auto \tab auto, exact, approx, hist, gpu_hist \tab - \cr