Explain overfitting and how to prevent it.

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Overfitting occurs when a machine learning model learns the training data too well, capturing noise and random fluctuations instead of the underlying patterns. As a result, the model performs very well on training data but poorly on new, unseen data because it fails to generalize.

Overfitting is more common when the model is too complex (e.g., having too many parameters) relative to the amount of training data or when the data is noisy.

Ways to prevent overfitting:

1. Cross-validation: Split the data into training and validation sets. Techniques like k-fold cross-validation help evaluate model performance on unseen data during training.

2. Simplify the model: Use a less complex model with fewer parameters to reduce the chance of fitting noise.

3. Regularization: Techniques like L1 (Lasso) and L2 (Ridge) regularization add a penalty for large weights in the model, discouraging complexity.

4. Pruning (for decision trees): Remove sections of the tree that provide little power to classify instances.

5. Early stopping: During training, stop the model once performance on the validation set starts to degrade, even if training accuracy continues to improve.

6. More training data: Feeding the model more diverse examples helps it learn the true patterns rather than memorizing the data.

7. Dropout (in neural networks): Randomly deactivate some neurons during training to prevent reliance on specific pathways.

Preventing overfitting ensures the model performs well not just on known data, but also on new, real-world inputs.

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