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Different methods have different handling of categorical predictors.Not all methods expect the same data format.Many methods have different cross-validation functions, or worse yet, no built-in process for cross-validation.The predict() function seems to have a different behavior for each new method we see.Calculate relevant metrics on the test dataĪt face value it would seem like it should be easy to repeat this process for a number of different methods, however we have run into a number of difficulties attempting to do so with R.Use resampling to find the “best model” by choosing the values of the tuning parameters.Decide on a set of candidate models (specify possible tuning parameters for method).Now that we have seen a number of classification and regression methods, and introduced cross-validation, we see the general outline of a predictive analysis: Discriminative versus Generative Methods.8.4 Estimating Expected Prediction Error.7.4 Tuning Parameters versus Model Parameters.7.1 Parametric versus Non-Parametric Models.6.4 Adding Flexibility to Linear Models.