dask_ml.naive_bayes.GaussianNB

class dask_ml.naive_bayes.GaussianNB(priors=None, classes=None)

Fit a naive bayes model with a Gaussian likelihood

Examples

>>> from dask_ml import datasets
>>> from dask_ml.naive_bayes import GaussianNB
>>> X, y = datasets.make_classification(chunks=50)
>>> gnb = GaussianNB()
>>> gnb.fit(X, y)

Methods

get_params([deep])

Get parameters for this estimator.

predict(X)

Perform classification on an array of test vectors X. Parameters ---------- X : array-like, shape = [n_samples, n_features] Returns ------- C : array, shape = [n_samples] Predicted target values for X.

predict_log_proba(X)

Return log-probability estimates for the test vector X. Parameters ---------- X : array-like, shape = [n_samples, n_features] Returns ------- C : array-like, shape = [n_samples, n_classes] Returns the log-probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.

predict_proba(X)

Return probability estimates for the test vector X. Parameters ---------- X : array-like, shape = [n_samples, n_features] Returns ------- C : array-like, shape = [n_samples, n_classes] Returns the probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.

set_params(**params)

Set the parameters of this estimator.

fit

__init__(priors=None, classes=None)