caret provides a variety of methods to use for comparing models. All of these methods are based on the resamples() function. My favorite is the box-and-whisker plot, which allows you to compare the distribution of predictive accuracy (in this case AUC) for the two models.

In general, you want the model with the higher median AUC, as well as a smaller range between min and max AUC.

You can make this plot using the bwplot() function, which makes a box and whisker plot of the model's out of sample scores. Box and whisker plots show the median of each distribution as a line and the interquartile range of each distribution as a box around the median line. You can pass the metric = "ROC" argument to the bwplot() function to show a plot of the model's out-of-sample ROC scores and choose the model with the highest median ROC.

If you do not specify a metric to plot, bwplot() will automatically plot 3 of them.

glmnet as a baseline model

Machine Learning with caret in R

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