Model validation

The goal of model validation is to evaluate whether the numerical results quantifying the hypothesized estimations/predictions of the trained model are acceptable descriptions of an independent dataset. The main reason is that any measure on the training set would be biased and optimistic since the model has already seen those observations. If we don't have a different dataset for validation, we can hold one fold of the data out from training and use it as benchmark. Another common technique is the cross-fold validation, and its stratified version, where the whole historical dataset is split into multiple folds. For simplicity, we will discuss the hold-one-out method; the same criteria apply also to the cross-fold validation. ...

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