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Overfitting (overfitting) occurs when the model fits the training data too well, losing the ability to generalize to new data. Methods to combat overfitting:

• Regularization — adding a penalty to the loss function for model complexity. For example, L1 (Lasso) and L2 (Ridge) regularizations reduce parameter weights, preventing over-complexity.

• Early stopping — stopping training when validation error starts to increase.

• Cross-validation — helps evaluate the model's generalization ability and select optimal hyperparameters.

• Data augmentation — creating additional training examples to increase data diversity.

• Dropout (for neural networks) — randomly disabling some neurons during training, reducing dependence on specific features.

• Reducing model complexity — choosing a simpler model with fewer parameters.

Example of L2 regularization in the loss function:

( Loss = Loss_{original} + \lambda \sum w_i^2 )

where ( \lambda ) is the regularization coefficient, ( w_i ) are the model weights.