Crossfold

Hardik Chheda Updated by Hardik Chheda

Crossfold performs k-fold cross-validation on a specified model in order to evaluate a model's ability to fit out-of-sample data.

The procedure splits the data randomly into k partitions, then for each partition it fits the specified model using the other k-1 groups and uses the resulting parameters to predict the dependent variable in the unused group.

Cross-validation is one of the most widely used data resampling methods to assess the generalization ability of a predictive model and to prevent overfitting. The purpose of cross-validation in the model building phase is to provide an estimate for the performance of this final model on a new data.

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Hyper parameter Tuning

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