The table below enumerates the models and the values of the method argument, as well as the complexity parameters used by train. The combination with the optimal resampling statistic is chosen as the final model and the entire training set is used to fit a final model.Ī variety of models are currently available. Across each data set, the performance of held-out samples is calculated and the mean and standard deviation is summarized for each combination. For particular model, a grid of parameters (if any) is created and the model is trained on slightly different data for each candidate combination of tuning parameters. maximize a logical recycled from the function arguments.ĭetails train can be used to tune models by picking the complexity parameters that are associated with the optimal resampling statistics.perfNames a character vector of performance metrics that are produced by the summary function.The returnResamp argument of trainControlĬontrols how much of the resampled results are saved. If leave-one-outĬross-validation or out-of-bag estimation methods are requested, resample A data frame with columns for each performance.finalModel an fit object using the best parameters.trControl the list of control parameters.metric a string that specifies what summary metric will be used to select the optimal model.call the (matched) function call with dots expanded.results a data frame the training error rate and values of the tuning parameters.modelType an identifier of the model type.A list is returned of class train containing:.(NOTE: If given, this argument must be named.) Value An integer denoting the number of levels for each tuning parameters that should be
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