Evaluation of Classification Models

A course by

Data Science Dojo

May, 2025 13 lessons English

Overview

Curriculum

In this module, we’ll focus on evaluating classification models and understanding their impact in real-world applications. We’ll begin by discussing why accuracy alone isn’t a reliable metric and how different types of errors affect business decisions. Through the confusion matrix and key performance metrics like precision, recall, and F1 score, we’ll learn to interpret model results with clarity. We’ll also explore advanced evaluation tools like ROC curves and AUC to help us assess model performance across thresholds. By the end of this module, you’ll be able to choose appropriate metrics, interpret model results effectively, and make evaluation decisions that align with real-world goals.

What You'll Learn

Explain why accuracy alone may be misleading when evaluating classification models
Interpret the confusion matrix to assess classification performance
Differentiate between key metrics such as precision, recall, and F1 score
Evaluate metric trade-offs in different business contexts
Apply ROC curves and AUC to compare model performance beyond threshold-based metrics