MACHINE LEARNING

Model Evaluation

Rigorously assess model performance and optimize hyperparameters to ensure robust and generalizable machine learning solutions.

~25 hours

Learning Objectives

Cross-ValidationPrecision, Recall, F1-ScoreROC and AUCHyperparameter Tuning

Perform grid search cross-validation on a support vector classifier and analyze the resulting confusion matrix.

📖 Learn by Reading

Evaluating Machine Learning Models

Alice Zheng

A concise and practical guide to selecting the right metrics for evaluating ML model performance.

🎥 Learn by Watching

Model Evaluation and Selection - Machine Learning

StatQuest with Josh Starmer • YouTube • 1:30:00

A clear, visual breakdown of cross-validation, ROC curves, and how to rigorously evaluate ML models.