Prepare

Due: September 5, 2024

Learning objectives

• Define metrics for evaluating the performance of classification models
• Identify trade-offs between different metrics
• Define overfitting and its implications for model evaluation
• Implement cross-validation to compare results across multiple models
• Review the random forest algorithm
• Define tuning parameters
• Implement grid search to optimize tuning parameters

Preparations

📖 Read

• TMWR ch 9-10, 12 - some is review, some is new. Skim what you feel comfortable, read a bit more closely stuff that is less familiar. Keep in mind a lot of the chapters are code examples. We will practice and extend the code in class so don’t feel like you need to have it memorized.

⌨️ Do

• Make sure you are registered for the class. Not on the wait list, but actually enrolled through Student Center.