PYTHON

decision tree classifier

# Import DecisionTreeClassifier from sklearn.tree
from sklearn.tree import DecisionTreeClassifier

# Instantiate a DecisionTreeClassifier 'dt' with a maximum depth of 6
dt = DecisionTreeClassifier(max_depth=6, criterion='entropy///gini', random_state=1)

# Fit dt to the training set
dt.fit(X_train, y_train)

# Predict test set labels
y_pred = dt.predict(X_test)
print(y_pred[0:5])
# Import accuracy_score
from sklearn.metrics import accuracy_score

# Compute test set accuracy  
acc = accuracy_score(y_test, y_pred)
print("Test set accuracy: {:.2f}".format(acc))

decision tree classifier example

# Create Decision Tree classifer object
clf = DecisionTreeClassifier()

# Train Decision Tree Classifer
clf = clf.fit(X_train,y_train)

#Predict the response for test dataset
y_pred = clf.predict(X_test)