Preventing overfitting requires a balanced approach: - Simpler Models: Start with simpler models before moving to more complex ones. - Cross-validation: Use techniques like k-fold cross-validation to ensure the model generalizes well. - Regularization: Apply methods like Lasso or Ridge Regression to penalize excessive complexity. - Pruning: In the case of decision trees, pruning can help remove unnecessary branches that lead to overfitting. - Data Augmentation: In some cases, increasing the size of the dataset through augmentation can help improve the model’s generalizability.
