Release 0.8.0

Besides Bernouilli naive bayes classifier and bootstrap aggregation algorithm, most notably Linfa's 0.8.0 release brings support for ndarray 0.16.

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

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