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 (KNNs) from scratch
- Non-parametric models – the pros and cons
