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Explain how cross-validation works and why it's important.
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Answer Posted / Raju Kumar Kushwaha
Cross-validation is a technique used to evaluate the performance of machine learning models by splitting the available data into multiple subsets. A common approach is k-fold cross-validation, where the original dataset is divided into k equal parts (folds). The model is then trained on k-1 folds and tested on the remaining fold. This process is repeated k times, ensuring that each fold is used for testing exactly once. Cross-validation helps to reduce overfitting by providing a more accurate estimate of a model's performance on unseen data.
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