Скачать или смотреть Using Cross-Validation to Detect Overfitting

Cross-validation is a powerful technique to detect overfitting by assessing how a model generalizes to an independent dataset. The most common method is k-fold cross-validation, where the dataset is divided into k subsets, and the model is trained and validated k times, each time using a different subset as the validation set and the others for training. This approach helps in reducing the variance associated with random train/test splits and provides a more reliable estimate of the model's performance on unseen data. By averaging the performance metrics across all folds, you can detect overfitting if the model performs significantly better on the training folds compared to the validation folds. Cross-validation is essential to ensure that your model's performance is robust and dependable.