#crossvalidation

Cross-validation stands as a cornerstone of robust machine learning model development, enabling us to assess how effectively a model generalizes to data it hasn’t encountered during training. It’s significantly more reliable than a single train/test split because repeated resampling offers a broader performance evaluation. Understanding and implementing various cross-validation techniques is…

CrossValidation : One should build the model completely based on Training data. The test data should probably not be used during model building and tuning phases.

One can use CrossValidation to tune parameters, choose the algorithm that needs to be used, etc.

K-fold CV

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  If K is large --  Majority of data in various training sets will be similar. The test data size for CV will be smaller. This will imply LOW BIAS and HIGH VARIANCE.

 If K is small --- Training and Test data can be significantly different in different CV situations. This will lead to HIGH BIAS and LOW VARIANCE

 High Var ----OVER FIT

Sometimes, as with cross-validation, one needs to generate k partitions, each with an equal number of observations.

There are probably an infinite number of ways this could be done in R, but the Gist below illustrates one way to do it in four lines, while ensuring that the partition counts are as equal as possible.