The Applied Data Science Workshop - Second Edition

5. Model Validation and Optimization

Overview

In this chapter, you will learn how to use k-fold cross validation to test model performance, as well as how to use validation curves to optimize model parameters. You will also learn how to implement dimensionality reduction techniques such as Principal Component Analysis (PCA). By the end of this chapter, you will have completed an end-to-end machine learning project and produced a final model that can be used to make business decisions.

Introduction

As we've seen in the previous chapters, it's easy to train models with scikit-learn using just a few lines of Python code. This is possible by abstracting away the computational complexity of the algorithm, including details such as constructing ...