Overfitting in supervised learning occurs when a model learns not only the underlying patterns in the training data but also the noise and outliers. This results in a model that performs very well on the training data but poorly on unseen data (test data).
Key Points:
- High Complexity: Overfitting often happens with complex models that have too many parameters relative to the amount of training data.
- Poor Generalization: The model fails to generalize well to new, unseen data, leading to high variance.
- Symptoms: You may notice a significant difference between training accuracy and test accuracy, where training accuracy is high, but test accuracy is low.
Prevention Techniques:
- Cross-Validation: Use techniques like k-fold cross-validation to ensure the model's performance is consistent across different subsets of the data.
- Regularization: Apply regularization techniques (like L1 or L2 regularization) to penalize overly complex models.
- Pruning: In decision trees, pruning can help reduce the complexity of the model.
- Early Stopping: Monitor the model's performance on a validation set and stop training when performance starts to degrade.
- Simpler Models: Start with simpler models and gradually increase complexity only if necessary.
By addressing overfitting, you can create models that are more robust and perform better on unseen data.
