2. Introduction to Scikit-Learn and Model Evaluation

Overview

After exploring the response variable of the case study data, this chapter introduces the core functionality of scikit-learn for training models and making predictions, through simple use cases of logistic and linear regression. Evaluation metrics for binary classification models, including true and false positive rates, the confusion matrix, the receiver operating characteristic (ROC) curve, and the precision-recall curve, are demonstrated both from scratch and using convenient scikit-learn functionality. By the end of this chapter, you'll be able to build and evaluate binary classification models using scikit-learn.