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Clojure for Machine Learning by Akhil Wali

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Using cross-validation

As we briefly mentioned earlier, cross-validation is a common validation technique that can be used to evaluate machine learning models. Cross-validation essentially measures how well the estimated model will generalize some given data. This data is different from the training data supplied to our model, and is called the cross-validation set, or simply validation set, of our model. Cross-validation of a given model is also called rotation estimation.

If an estimated model performs well during cross-validation, we can assume that the model can understand the relationship between its various independent and dependent variables. The goal of cross-validation is to provide a test to determine if a formulated model is overfit ...

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