Introduction: scikit-learn and Model Evaluation
Explore the basics of scikit-learn for building and evaluating binary classification models. Understand key evaluation metrics including true and false positive rates, confusion matrix, ROC curve, and precision-recall curve. Gain foundational skills to assess model performance using practical examples.
We'll cover the following...
We'll cover the following...
Overview
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 ...