python naive bayes classification example

Example 1: write a Program in Python/R to Demonstrate naive bayes classification

>>> from sklearn.naive_bayes import GaussianNB
>>> from sklearn.naive_bayes import MultinomialNB
>>> from sklearn import datasets
>>> from sklearn.metrics import confusion_matrix
>>> from sklearn.model_selection import train_test_split

>>> iris = datasets.load_iris()
>>> X = iris.data
>>> y = iris.target

# Split the data into a training set and a test set
>>> X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
>>> gnb = GaussianNB()
>>> mnb = MultinomialNB()

>>> y_pred_gnb = gnb.fit(X_train, y_train).predict(X_test)
>>> cnf_matrix_gnb = confusion_matrix(y_test, y_pred_gnb)

>>> print(cnf_matrix_gnb)
[[16 0 0]
 [ 0 18 0]
 [ 0 0 11]]

>>> y_pred_mnb = mnb.fit(X_train, y_train).predict(X_test)
>>> cnf_matrix_mnb = confusion_matrix(y_test, y_pred_mnb)

>>> print(cnf_matrix_mnb)
[[16 0 0]
 [ 0 0 18]
 [ 0 0 11]]Copy

Example 2: feature importance naive bayes python

neg_class_prob_sorted = NB_optimal.feature_log_prob_[0, :].argsort()[::-1]
pos_class_prob_sorted = NB_optimal.feature_log_prob_[1, :].argsort()[::-1]

print(np.take(count_vect.get_feature_names(), neg_class_prob_sorted[:10]))
print(np.take(count_vect.get_feature_names(), pos_class_prob_sorted[:10]))