14 Great Articles About Cross-Validation, Model Fitting and Selection
Cross-validation is a technique used to assess the accuracy of a predictive model, based on training set data. It splits the training sets into test and control sets. The test sets are used to fine-tune the model to increase performance (better classification rate or reduced errors in prediction) and the control sets are used to simulate how the model would perform outside the training set. The control and test sets must be carefully chosen for this method to make sense.
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Link: 14 Great Articles About Cross-Validation, Model Fitting and Selection