Validation supervised model This is my first approact to Machine Learning. I want to know if there are other methods besides train, validation and test.
Thanks.

In the phase of study and analysis of the problem it is possible to test different models of classification. Then the problem arises of how to understand if a model is working better than another and how to choose the best one among those tested. There are numerous methodologies of selection of the model, between which:

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*Leave-One-Out Cross Validation (LOOCV);

*K-fold Cross Validation (KCV).

The logic Leave-One-Out Cross-Validation is able to overcome the limitations of the previous methodology, in fact as a validation set takes into account only one element of the total dataset. Consequently, it is possible to perform n validation tests, where n represents the numerosity of the total dataset.
In this way at each iteration the model estimates the class of the n-th sample. Then the prediction error is calculated verifying the goodness of the answer provided by the model. The total error of the model is given by an average of the n errors calculated in the samples.
Finally, the K-fold Cross-Validation is basically an evolution of the previous method. The total dataset is divided into k partitions, where k is a reasonable number that allows for a good amount of data within each partition. The method follows an iterative logic in which one partition at a time is used as a test and calculated, accordingly, the prediction error.
