Model Evaluation & Metrics

This tutorial explains how to evaluate machine learning models using the most important regression and classification metrics. It covers MSE, RMSE, MAE, and R² for regression, as well as Accuracy, Precision, Recall, F1-score, Confusion Matrix, and ROC-AUC for classification, with clear explanations and practical visualizations using Matplotlib and Seaborn.

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🧠 Start Quiz – Test Your Knowledge

Quiz

This QCM set contains 15 multiple-choice questions designed to test understanding of key concepts in model evaluation and metrics. It covers regression metrics, classification metrics, PR-AUC for imbalanced data, multi-class averaging methods, and cross-validation, helping learners reinforce both theoretical knowledge and practical interpretation.

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