Overfitting and underfitting

Overfitting is memorising the training data; underfitting is not learning enough from it.

• Overfit: great on training data, poor on test data.
• Underfit: poor on both.
• Fixes for overfitting: more data, a simpler model, regularisation.

Example: Train 99%, test 70% → overfitting. Train 65%, test 63% → underfitting.

Remember: Compare training and test scores. A big gap means overfitting.

Common mistake: Making the model more complex when it's already overfitting.