Introduction

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 data. In this chapter, you will learn how to evaluate the performance of the various models and choose the most appropriate one. Choosing an appropriate machine learning model is an art that requires experience, and each algorithm has its own advantages and disadvantages.

Picking the right performance metrics, optimizing, fine-tuning, and evaluating the model is an important part of building any supervised machine learning model. We will start by using the most common Python machine learning API, scikit-learn, to build our logistic regression model, then we will learn different classification algorithm, and the intuition behind them, and finally, we will learn how to optimize, evaluate, and choose the best model.