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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. https://www.sevenmentor.com/da....ta-science-course-in
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