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Author: relf

docs/website/content/news/release_080.md

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+title = "Release 0.8.0"
+date = "2025-09-30"
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+
+Besides Bernouilli naive bayes classifier and bootstrap aggregation algorithm, most notably Linfa's 0.8.0 release brings support for `ndarray` 0.16.
+
+<!-- more -->
+
+## Improvements and fixes
+
+ * add `max_features` and `tokenizer_function` to `CountVectorizer` in `linfa-preprocessing`
+ * add `predict_proba()` to `Gaussian mixture model` in `linfa-clustering` 
+ * add `predict_proba()` and `predict_log_proba()` to algorithms in `linfa-bayes`
+ * add target names to `dataset`
+ * fix SVR parameterization in `linfa-svm`
+ * fix serde support for algorithms in `linfa-pls`
+ * fix confusion matrix: use predicted and ground thruth labels, make it reproducible
+ * fix dataset names after shuffling 
+ * bump `ndarray` to 0.16, `argmin` to 0.11.0, `kdtree` to 0.7.0, statrs to `0.18`, sprs to `0.11`
+ * bump MSRV to 1.87.0
+
+## New algorithms
+
+### Bernouilli Naive Bayes
+
+Naive Bayes for Bernouilli models is a classification algorithm for data that is distributed according to multivariate Bernoulli distributions; 
+i.e., there may be multiple features but each one is assumed to be a binary-valued (Bernoulli, boolean) variable. 
+
+See [scikit-learn.naive_bayes](https://scikit-learn.org/stable/modules/naive_bayes.html)
+
+### Bootstrap aggregation 
+
+In ensemble algorithms, bagging (Bootstrap aggregating) methods form a class of algorithms which build several instances of a black-box 
+estimator on random subsets of the original training set and then aggregate their individual predictions to form a final prediction.
+
+See [sklearn.ensemble](https://scikit-learn.org/stable/modules/ensemble.html)