Summary  
This chapter explains how to implement code to compute and print classification evaluation metrics—such as accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC—to assess model performance.

General domain of usage  
Machine learning model evaluation

This course provides a comprehensive introduction to supervised learning using Python. You will explore the fundamental concepts, algorithms, and evaluation techniques that underpin supervised machine learning, with practical examples using scikit-learn and visualization tools.

Explore the foundational concepts of supervised learning, including its definition, types of problems, and real-world applications.

Learn the essential steps for preparing data for supervised learning, including handling missing values, encoding categorical variables, and feature scaling.

Dive into the most widely used supervised learning algorithms, including their principles and implementation in Python.

Master the techniques for evaluating supervised learning models and ensuring their generalizability.

Learn to use visualization tools to interpret and communicate the results of supervised learning models.

Evaluation Metrics for Classification