Overfitting occurs when a model learns not only the underlying pattern of the data but also the noise and anomalies, leading to poor performance on new, unseen data. Techniques to prevent overfitting include:
1. Using more training data.
2. Cross - validation.
3. Regularization(L1 / L2).
4. Pruning decision trees.
5. Early stopping in neural networks.