docs: add link to new book chapter about internal tuning

mlr-org · Nov 7, 2024 · 4dc1160 · 4dc1160
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diff --git a/README.Rmd b/README.Rmd
@@ -50,13 +50,11 @@ There are several sections about hyperparameter optimization in the [mlr3book](h
 * Learn about [tuning spaces](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html#sec-defining-search-spaces).
 * Estimate the model performance with [nested resampling](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html#sec-nested-resampling).
 * Learn about [multi-objective optimization](https://mlr3book.mlr-org.com/chapters/chapter5/advanced_tuning_methods_and_black_box_optimization.html#sec-multi-metrics-tuning).
+* Simultaneously optimize hyperparameters and use [early stopping](https://mlr3book.mlr-org.com/chapters/chapter15/predsets_valid_inttune.html) with XGBoost.
 
 The [gallery](https://mlr-org.com/gallery-all-optimization.html) features a collection of case studies and demos about optimization.
 
 * Learn more advanced methods with the [Practical Tuning Series](https://mlr-org.com/gallery/series/2021-03-09-practical-tuning-series-tune-a-support-vector-machine/).
-* Optimize an rpart classification tree with only a [few lines of code](https://mlr-org.com/gallery/optimization/2022-11-10-hyperparameter-optimization-on-the-palmer-penguins/).
-* Simultaneously optimize hyperparameters and use [early stopping](https://mlr-org.com/gallery/optimization/2022-11-04-early-stopping-with-xgboost/) with XGBoost.
-* Make us of proven [search space](https://mlr-org.com/gallery/optimization/2021-07-06-introduction-to-mlr3tuningspaces/).
 * Learn about [hotstarting](https://mlr-org.com/gallery/optimization/2023-01-16-hotstart/) models.
 * Run the [default hyperparameter configuration](https://mlr-org.com/gallery/optimization/2023-01-31-default-configuration/) of learners as a baseline.
 * Use the [Hyperband](https://mlr-org.com/gallery/series/2023-01-15-hyperband-xgboost/) optimizer with different budget parameters.

diff --git a/README.md b/README.md
@@ -34,53 +34,49 @@ The package is built on the optimization framework
 
 mlr3tuning is extended by the following packages.
 
--   [mlr3tuningspaces](https://github.com/mlr-org/mlr3tuningspaces) is a
-    collection of search spaces from scientific articles for commonly
-    used learners.
--   [mlr3hyperband](https://github.com/mlr-org/mlr3hyperband) adds the
-    Hyperband and Successive Halving algorithm.
--   [mlr3mbo](https://github.com/mlr-org/mlr3mbo) adds Bayesian
-    Optimization methods.
+- [mlr3tuningspaces](https://github.com/mlr-org/mlr3tuningspaces) is a
+  collection of search spaces from scientific articles for commonly used
+  learners.
+- [mlr3hyperband](https://github.com/mlr-org/mlr3hyperband) adds the
+  Hyperband and Successive Halving algorithm.
+- [mlr3mbo](https://github.com/mlr-org/mlr3mbo) adds Bayesian
+  Optimization methods.
 
 ## Resources
 
 There are several sections about hyperparameter optimization in the
 [mlr3book](https://mlr3book.mlr-org.com).
 
--   Getting started with [hyperparameter
-    optimization](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html).
--   An overview of all tuners can be found on our
-    [website](https://mlr-org.com/tuners.html).
--   [Tune](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html#sec-model-tuning)
-    a support vector machine on the Sonar data set.
--   Learn about [tuning
-    spaces](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html#sec-defining-search-spaces).
--   Estimate the model performance with [nested
-    resampling](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html#sec-nested-resampling).
--   Learn about [multi-objective
-    optimization](https://mlr3book.mlr-org.com/chapters/chapter5/advanced_tuning_methods_and_black_box_optimization.html#sec-multi-metrics-tuning).
+- Getting started with [hyperparameter
+  optimization](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html).
+- An overview of all tuners can be found on our
+  [website](https://mlr-org.com/tuners.html).
+- [Tune](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html#sec-model-tuning)
+  a support vector machine on the Sonar data set.
+- Learn about [tuning
+  spaces](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html#sec-defining-search-spaces).
+- Estimate the model performance with [nested
+  resampling](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html#sec-nested-resampling).
+- Learn about [multi-objective
+  optimization](https://mlr3book.mlr-org.com/chapters/chapter5/advanced_tuning_methods_and_black_box_optimization.html#sec-multi-metrics-tuning).
+- Simultaneously optimize hyperparameters and use [early
+  stopping](https://mlr3book.mlr-org.com/chapters/chapter15/predsets_valid_inttune.html)
+  with XGBoost.
 
 The [gallery](https://mlr-org.com/gallery-all-optimization.html)
 features a collection of case studies and demos about optimization.
 
--   Learn more advanced methods with the [Practical Tuning
-    Series](https://mlr-org.com/gallery/series/2021-03-09-practical-tuning-series-tune-a-support-vector-machine/).
--   Optimize an rpart classification tree with only a [few lines of
-    code](https://mlr-org.com/gallery/optimization/2022-11-10-hyperparameter-optimization-on-the-palmer-penguins/).
--   Simultaneously optimize hyperparameters and use [early
-    stopping](https://mlr-org.com/gallery/optimization/2022-11-04-early-stopping-with-xgboost/)
-    with XGBoost.
--   Make us of proven [search
-    space](https://mlr-org.com/gallery/optimization/2021-07-06-introduction-to-mlr3tuningspaces/).
--   Learn about
-    [hotstarting](https://mlr-org.com/gallery/optimization/2023-01-16-hotstart/)
-    models.
--   Run the [default hyperparameter
-    configuration](https://mlr-org.com/gallery/optimization/2023-01-31-default-configuration/)
-    of learners as a baseline.
--   Use the
-    [Hyperband](https://mlr-org.com/gallery/series/2023-01-15-hyperband-xgboost/)
-    optimizer with different budget parameters.
+- Learn more advanced methods with the [Practical Tuning
+  Series](https://mlr-org.com/gallery/series/2021-03-09-practical-tuning-series-tune-a-support-vector-machine/).
+- Learn about
+  [hotstarting](https://mlr-org.com/gallery/optimization/2023-01-16-hotstart/)
+  models.
+- Run the [default hyperparameter
+  configuration](https://mlr-org.com/gallery/optimization/2023-01-31-default-configuration/)
+  of learners as a baseline.
+- Use the
+  [Hyperband](https://mlr-org.com/gallery/series/2023-01-15-hyperband-xgboost/)
+  optimizer with different budget parameters.
 
 The [cheatsheet](https://cheatsheets.mlr-org.com/mlr3tuning.pdf)
 summarizes the most important functions of mlr3tuning.
@@ -161,7 +157,7 @@ tuner$optimize(instance)
 ```
 
     ##        cost     gamma learner_param_vals  x_domain classif.ce
-    ## 1: 5.756463 -5.756463          <list[4]> <list[2]>  0.2063492
+    ## 1: 5.756463 -5.756463          <list[4]> <list[2]>  0.1828847
 
 The tuner returns the best hyperparameter configuration and the
 corresponding measured performance.
@@ -172,18 +168,18 @@ The archive contains all evaluated hyperparameter configurations.
 as.data.table(instance$archive)[, .(cost, gamma, classif.ce, batch_nr, resample_result)]
 ```
 
-    ##           cost     gamma classif.ce batch_nr  resample_result
-    ##  1:   5.756463  0.000000  0.4661146        1 <ResampleResult>
-    ##  2:  11.512925  0.000000  0.4661146        2 <ResampleResult>
-    ##  3: -11.512925  5.756463  0.4661146        3 <ResampleResult>
-    ##  4: -11.512925  0.000000  0.4661146        4 <ResampleResult>
-    ##  5:  -5.756463 11.512925  0.4661146        5 <ResampleResult>
-    ## ---                                                          
-    ## 21:  11.512925  5.756463  0.4661146       21 <ResampleResult>
-    ## 22: -11.512925 11.512925  0.4661146       22 <ResampleResult>
-    ## 23:   0.000000  5.756463  0.4661146       23 <ResampleResult>
-    ## 24:   5.756463 11.512925  0.4661146       24 <ResampleResult>
-    ## 25: -11.512925 -5.756463  0.4661146       25 <ResampleResult>
+    ##           cost      gamma classif.ce batch_nr  resample_result
+    ##  1:  -5.756463   5.756463  0.4663216        1 <ResampleResult>
+    ##  2:   5.756463  -5.756463  0.1828847        2 <ResampleResult>
+    ##  3:  11.512925   5.756463  0.4663216        3 <ResampleResult>
+    ##  4:   5.756463  11.512925  0.4663216        4 <ResampleResult>
+    ##  5: -11.512925 -11.512925  0.4663216        5 <ResampleResult>
+    ## ---                                                           
+    ## 21:  -5.756463  -5.756463  0.4663216       21 <ResampleResult>
+    ## 22:  11.512925  11.512925  0.4663216       22 <ResampleResult>
+    ## 23: -11.512925  11.512925  0.4663216       23 <ResampleResult>
+    ## 24:  11.512925  -5.756463  0.1828847       24 <ResampleResult>
+    ## 25:   0.000000  -5.756463  0.2402346       25 <ResampleResult>
 
 The [mlr3viz](https://mlr3viz.mlr-org.com/) package visualizes tuning
 results.