![]() ![]() List of all the classes that can possibly appear in the y vector. classes array-like of shape (n_classes,), default=None Training vectors, where n_samples is the number of samples and Parameters : X array-like of shape (n_samples, n_features) Hence it is better to call partial_fit on chunks of data that areĪs large as possible (as long as fitting in the memory budget) to This method has some performance and numerical stability overhead, ![]() This is especially useful when the whole dataset is too big to fit in On different chunks of a dataset so as to implement out-of-core This method is expected to be called several times consecutively partial_fit ( X, y, classes = None, sample_weight = None ) ¶ If True, will return the parameters for this estimator andĬontained subobjects that are estimators. Please check User Guide on how the routing New in version 0.17: Gaussian Naive Bayes supports fitting with sample_weight. Weights applied to individual samples (1. sample_weight array-like of shape (n_samples,), default=None Training vectors, where n_samples is the number of samplesĪnd n_features is the number of features. Request metadata passed to the score method.įit ( X, y, sample_weight = None ) ¶įit Gaussian Naive Bayes according to X, y. Request metadata passed to the partial_fit method. Request metadata passed to the fit method. Return the mean accuracy on the given test data and labels. Return probability estimates for the test vector X. Return log-probability estimates for the test vector X. Return joint log probability estimates for the test vector X. Perform classification on an array of test vectors X. predict (])) įit Gaussian Naive Bayes according to X, y. ![]() unique ( Y )) GaussianNB() > print ( clf_pf. predict (])) > clf_pf = GaussianNB () > clf_pf. fit ( X, Y ) GaussianNB() > print ( clf. array () > from sklearn.naive_bayes import GaussianNB > clf = GaussianNB () > clf. ![]()
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