The data set is separated into two sets, called the training set and the testing set. ╔ The holdout method is the simplest kind of cross validation. This is the basic idea for a whole class of model evaluation methods called cross validation. Then when training is done, the data that was removed can be used to test the performance of the learned model on ``new'' data. ![]() Some of the data is removed before training begins. One way to overcome this problem is to not use the entire data set when training a learner. The problem with residual evaluations is that they do not give an indication of how well the learner will do when it is asked to make new predictions for data it has not already seen. But the predictions from the model on new data will usually get worse as higher order terms are added.Ĭross validation is a model evaluation method that is better than residuals. For example, in a simple polynomial regression I can just keep adding higher order terms and so get better and better fits to the data. It is easy to over-fit the data by including too many degrees of freedom and so inflate R2 and other fit statistics. ![]() It might be helpful to summarize the role of cross-validation in statistics.Ĭross-validation is primarily a way of measuring the predictive performance of a statistical model.Įvery statistician knows that the model fit statistics are not a good guide to how well a model will predict: high R2 does not necessarily mean a good model. Surprisingly, many statisticians see cross-validation as something data miners do, but not a core statistical technique. Cross-validation is a process by which a method that works for one sample of a population is checked for validity by applying the method to another sample from the same population.
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