Chapter 5

Ensemble Modeling

Learning Objectives

By the end of the chapter, you will be able to:

  • Explain the concepts of bias and variance and how they lead to underfitting and overfitting
  • Explain the concepts behind bootstrapping
  • Implement a bagging classifier using decision trees
  • Implement adaptive boosting and gradient boosting models
  • Implement a stacked ensemble using a number of classifiers

This chapter covers bias and variance, and underfitting and overfitting, and then introduces ensemble modeling.


In the previous chapters, we discussed the two types of supervised learning problems: regression and classification. We looked at a number of algorithms for each type and delved into how those algorithms worked.

But there ...

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