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Can anyone explain cross-validation in machine learning?

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Cross-validation in Machine Learning is used to reduce the overfitting of data. In cross-validation, we divide the population into k-subsets. We built a model on one subset and validate on k-1 subsets. So, we will get k models at the end. We calculate the performance metric of all models and consider the average of that metric as an overall performance metric of our model.

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