+++
title = "Release 0.8.0"
date = "2025-09-30"
+++
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](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)