27th Sept 2023

Cross-validation is a crucial technique in machine learning used to assess the performance and generalization of a predictive model. It involves splitting the dataset into multiple subsets, typically a training set and a validation set, several times. The model is trained on different combinations of these subsets, allowing it to learn and evaluate its performance on various portions of the data. This process helps detect overfitting (when the model performs well on the training data but poorly on new data) and provides a more robust estimate of a model’s accuracy. Common types of cross-validation include k-fold cross-validation, where the data is divided into k subsets, and leave-one-out cross-validation, where each data point serves as the validation set once. Cross-validation is essential for selecting the best model and hyperparameters while ensuring the model’s ability to generalize to unseen data.