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Cross-Validation
What is it and why use it?
Regression and classification Machine Learning models aim to predict a value or class from the variables contained in the data. Each model has its own algorithm to try to identify the patterns contained in the data that allow an accurate prediction to be made.
The models, in addition to being accurate, must be generalist, with the ability to interpret data never seen before and reach an adequate result. One way of evaluating this generalization capacity of the models is to apply Cross-Validation.
But what is Cross-Validation?
Cross-validation is a technique used as a way of obtaining an estimate of the overall performance of the model. There are several Cross-Validation techniques, but they basically consist of separating the data into training and testing subsets.
The training subset, as the name implies, will be used during the training process to calculate the hyperparameters of the model. To calculate the generalization capacity of the model, after the training stage, the test model is used.
