A random forest is an ensemble classifier that estimates based on the combination of different decision trees. Effectively, it fits a number of decision tree classifiers on various subsamples of the dataset. Also, each tree in the forest built on a random best subset of features. Finally, the act of enabling these trees gives us the best subset of features among all the random subsets of features. Random forest is currently one of best performing algorithms for many classification problems.

An example of Random forest is as follows:

>>>from sklearn.ensemble import RandomForestClassifier
>>>RF_clf = RandomForestClassifier(n_estimators=10)
>>>predicted = RF_clf.predict(X_test)
>>>print '
 Here is the classification report:'
>>>print classification_report(y_test, predicted)
>>>cm = confusion_matrix(y_test, y_pred)
>>>print cm