Hands-On Machine Learning for Cybersecurity

Random forest

Random forests are ensemble learning methods that are used for either classification or regression purposes. Random forests are composed of several decision trees that are combined together to make a unanimous decision or classification. Random forest are better than just regular decision trees because they do not cause overfitting of the data:

We would then try to use the random forest classifier:

from sklearn.ensemble import RandomForestClassifier rf = RandomForestClassifier()scores = cross_val_score(rf, X, y, scoring='accuracy', cv=5)print np.mean(scores)  # nicerrf.fit(X, y)

The output can be seen as follows:

Out[39]:0.9997307783262454 ...