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from sklearn.ensemble import RandomForestClassifier

forest = RandomForestClassifier(criterion='entropy', 
                                n_estimators=10, 
                                random_state=1,
                                n_jobs=2)
'''
-criteria are “gini” for the Gini impurity and “entropy” for the information gain
-number of features d at each split = sqrt(number of features in traingset) by default
-n_estimators = number of trees in forest (10 by default)
-n_jobs = number of jobs to run in parallel for both fit and predict
'''

forest.fit(X_train, y_train)