Cross validation provides a more reliable estimate of a machine learning model’s ability to perform on data it does not have access to in the training process. Evaluating a model’s performance on the same data in which it is trained has inherent bias and is not guaranteed to be representative of its ability to generalize to new data.
How does Cross Validation Work?
Cross validation provides a more reliable estimate of a machine learning model’s ability to perform on data it does not have access to in the training process. Evaluating a model’s performance on the same data in which it is trained has inherent bias and is not guaranteed to be representative of its ability to generalize to new data.
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