Overfitting and underfitting: A model that memorizes training data instead of learning the pattern (overfit), or one too weak to learn it at all (underfit).
Leads to
Hyperparameter tuning: Systematically searching for the settings (learning rate, regularization strength, tree depth) that aren't learned from data but chosen before training.
Related topics
Hyperparameter tuning: Systematically searching for the settings (learning rate, regularization strength, tree depth) that aren't learned from data but chosen before training.