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Dec 27, 2019 in Data Science
Q: Explain cross-validation

1 Answer

Dec 27, 2019

Cross-validation is a model validation technique for evaluating how the outcomes of statistical analysis will generalize to an independent dataset. Mainly used in backgrounds where the objective is forecast and one wants to estimate how accurately a model will accomplish in practice.

The goal of cross-validation is to term a data set to test the model in the training phase (i.e. validation data set) in order to limit problems like overfitting and get an insight on how the model will generalize to an independent data set.

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