Cross-Validation

Cross-validation is a statistical method used to evaluate the performance and accuracy of a machine learning model. It involves partitioning the dataset into subsets, where one subset is used for testing the model and the remaining subsets are used for training the model. This process is repeated multiple times, with each subset being used for testing at least once. The results are then averaged to provide an estimate of the model's performance. Cross-validation helps 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. It also helps to ensure that the model is generalizable and can perform well on new, unseen data.
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