Supervised Machine Learning with Python

Working with Non-Parametric Models

In the last chapter, we introduced parametric models and explored how to implement linear and logistic regression. In this chapter, we will cover the non-parametric model family. We will start by covering the bias-variance trade-off, and explaining how parametric and non-parametric models differ at a fundamental level. Later, we'll get into decision trees and clustering methods. Finally, we'll address some of the pros and cons of the non-parametric models.

In this chapter, we will cover the following topics:

• The bias/variance trade-off
• An introduction to non-parametric models and decision trees
• Decision trees
• Implementing a decision tree from scratch
• Various clustering methods
• Implementing K-Nearest Neighbors ...