Grokking Machine Learning

12 Combining models to maximize results: Ensemble learning

In this chapter

• what ensemble learning is, and how it is used to combine weak classifiers into a stronger one
• using bagging to combine classifiers in a random way
• using boosting to combine classifiers in a cleverer way
• some of the most popular ensemble methods: random forests, AdaBoost, gradient boosting, and XGBoost

After learning many interesting and useful machine learning models, it is natural to wonder if it is possible to combine these classifiers. Thankfully, we can, and in this chapter, we learn several ways to build stronger models by combining weaker ones. The two main methods ...