Chapter 8

Fine-Tuning Classification Algorithms

Learning Objectives

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

  • Use some of the most common classification algorithms from the scikit-learn machine learning library
  • Describe the logic behind tree-based models
  • Choose the performance metrics required for classification problems
  • Optimize and evaluate the best classification algorithm for customer churn prediction

This chapter covers other classification algorithms such as support vector machines, decision trees, random forest, and explains how to evaluate them.


In the previous chapter, you learned about the most common data science pipeline: OSEMN. You also learned how to pre-process, explore, model, and finally, interpret ...

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