What is cross-validation, and why is it important?

Cross-validation is a fundamental technique in machine learning and statistical modeling used to assess the performance of a model on unseen data. It is particularly useful in preventing overfitting, ensuring that a model generalizes well to new datasets. The core idea of cross-validation is to divide the dataset into multiple subsets or folds, training the model on some of these subsets while validating its performance on the remaining ones. This process is repeated multiple times, and the results are averaged to obtain a reliable estimate of the model’s effectiveness.

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