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A more sophisticated version of training/test sets is time series cross-validation. In this procedure, there are a series of test sets, each consisting of a. Cross-validation, sometimes called rotation estimation is a resampling validation technique for assessing how the results of a statistical analysis will. Cross Validation Scores Generally we determine whether a given model is optimal by looking at it's F1, precision, recall, and accuracy (for classification).

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A more sophisticated version of training/test sets is time series cross-validation. In this procedure, there are a series of test sets, each consisting of a. Until now we have used the simplest of all cross-validation methods, which consists in testing our predictive models on a subset of the data (the test set). The cross-validation is a repetition of the process above but each time we use a different split of the data. This will result in several measures of.

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The cross-validation is a repetition of the process above but each time we use a different split of the data. This will result in several measures of. Cross validation is not used to avoid over-fitting. It's done to get an accurate assessment of the accuracy of a system. However, there is a closely related. Cross validation can be used to select the best model configuration and/or evaluate model performance. What is the problem with doing both simultaneously? If.