Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition

Chapter 7. Ensemble Learning and Random Forests

Suppose you pose a complex question to thousands of random people, then aggregate their answers. In many cases you will find that this aggregated answer is better than an expert’s answer. This is called the wisdom of the crowd. Similarly, if you aggregate the predictions of a group of predictors (such as classifiers or regressors), you will often get better predictions than with the best individual predictor. A group of predictors is called an ensemble; thus, this technique is called ensemble learning, and an ensemble learning algorithm is called an ensemble method.

As an example of an ensemble method, you can train a group of decision tree classifiers, each on a different random subset of the training set. You can then obtain the predictions of all the individual trees, and the class that gets the most votes is the ensemble’s prediction (see the last exercise in Chapter 6). Such an ensemble of decision trees is called a random forest, and despite its simplicity, this is one of the most powerful machine learning algorithms available today.

As discussed in Chapter 2, you will often use ensemble methods near the end of a project, once you have already built a few good predictors, to combine them into an even better predictor. In fact, the winning solutions in machine learning competitions often involve several ensemble methods—most famously in the Netflix Prize competition.

In this chapter we will examine the most popular ensemble ...