This is an old revision of the document!
Scikit-learn provides both Gaussian and multinomial/binomial flavors of Naive Bayes. Here's a code snippet that uses the Gaussian version and shows the resulting decision boundary for toy 2-d data (it uses ``decision_boundary.py`` from a previous demo):
import numpy as np from sklearn.datasets import make_blobs from sklearn.naive_bayes import GaussianNB import decision_boundary X, y = make_blobs(n_samples=1500, random_state=2) classifier = GaussianNB() decision_boundary.plot_boundary(classifier, X, y) transformation = [[ 0.60834549, -0.63667341], [-0.40887718, 0.85253229]] X, y = make_blobs(n_samples=1500, random_state=170) X_aniso = np.dot(X, transformation) decision_boundary.plot_boundary(classifier, X_aniso, y) X_varied, y_varied = make_blobs(n_samples=1500, cluster_std=[1.0, 2.5, 0.5], random_state=170) decision_boundary.plot_boundary(classifier, X_varied, y_varied)