Fundamentals of Model Validation

Explain the purpose of splitting data into training, validation, and test sets.

Perform k-fold cross-validation to get a robust estimate of model performance.

Define bias and variance and their relationship to underfitting and overfitting.

Diagnose underfitting and overfitting by comparing training and validation performance.

Plot and interpret learning curves to diagnose bias and variance issues.

Explain the purpose of a baseline model and establish one for a given problem.