Understanding the Power of the Many
IN THIS CHAPTER
Understanding how a decision tree works
Using Random Forest and other bagging techniques
Taking advantage of the most performing ensembles by boosting
In this chapter, you go beyond the single machine learning models you’ve seen until now and explore the power of ensembles, which are groups of models that can outperform single models. Ensembles work like the collective intelligence of crowds, using pooled information to make better predictions. The basic idea is that a group of nonperforming algorithms can produce better results than a single well-trained model.
Maybe you’ve participated in one of those games that ask you to guess the number of sweets in a jar at parties or fairs. Even though a single person has a slim chance of guessing the right number, various experiments have confirmed that if you take the wrong answers of a large number of game participants and average them, you can get close to the...