Cross-validation Techniques

Cross-validation is a statistical method used to evaluate the performance of machine learning models. It involves partitioning the dataset into subsets, training the model on one subset (called the training set), and testing the model on another subset (called the validation set). This process is repeated multiple times, with different subsets used for training and validation each time. The results are then averaged to give an estimate of the model's performance. Cross-validation is used to prevent overfitting, which occurs when a model is too complex and fits the training data too closely, resulting in poor performance on new data. There are several types of cross-validation techniques, including k-fold cross-validation, leave-one-out cross-validation, and stratified cross-validation.
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