Naive Bayes - sklearn

scikit_nb.txt

# load the iris dataset 
from sklearn.datasets import load_iris 
iris = load_iris() 
  
# store the feature matrix (X) and response vector (y) 
X = iris.data 
y = iris.target 
  
# splitting X and y into training and testing sets 
from sklearn.model_selection import train_test_split 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=1) 
  
# training the model on training set 
from sklearn.naive_bayes import GaussianNB 
gnb = GaussianNB() 
gnb.fit(X_train, y_train) 
  
# making predictions on the testing set 
y_pred = gnb.predict(X_test) 
  
# comparing actual response values (y_test) with predicted response values (y_pred) 
from sklearn import metrics 
print("Gaussian Naive Bayes model accuracy(in %):", metrics.accuracy_score(y_test, y_pred)*100)