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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.
max_features and tokenizer_function to CountVectorizer in linfa-preprocessingpredict_proba() to Gaussian mixture model in linfa-clusteringpredict_proba() and predict_log_proba() to algorithms in linfa-bayesdatasetlinfa-svmlinfa-plsndarray to 0.16, argmin to 0.11.0, kdtree to 0.7.0, statrs to 0.18, sprs to 0.11Naive 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.
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