What is Cross-Validation?
Cross-validation is a statistical method used to evaluate the performance of machine learning models. Its main purpose is to ensure that a model generalizes well to an independent dataset, thereby preventing overfitting. The approach involves dividing the dataset into multiple subsets or "folds," using some of these for training and others for testing.
The most common cross-validation technique is k-fold cross-validation. In this method, the dataset is divided into k equal-sized folds. The model is trained on k-1 folds and validated on the remaining fold. This process is repeated k times, with each fold serving as the validation set once. The results from the k iterations are then averaged to produce a more reliable estimate of the model’s performance.
Other variations of cross-validation include stratified k-fold, which preserves the percentage of samples for each class in classification problems, and leave-one-out cross-validation, where each individual data point is used as the validation set once. Cross-validation helps in tuning hyperparameters and selecting the best model by providing insights into how the model performs on unseen data.
Overall, cross-validation is a fundamental practice in machine learning that enhances the robustness and credibility of predictive models.