Plot an ROC curve

As you saw in the video, an ROC curve is a really useful shortcut for summarizing the performance of a classifier over all possible thresholds. This saves you a lot of tedious work computing class predictions for many different thresholds and examining the confusion matrix for each.

My favorite package for computing ROC curves is caTools, which contains a function called colAUC(). This function is very user-friendly and can actually calculate ROC curves for multiple predictors at once. In this case, you only need to calculate the ROC curve for one predictor, e.g.:

colAUC(predicted_probabilities, actual, plotROC = TRUE)

The function will return a score called AUC (more on that later) and the plotROC = TRUE argument will return the plot of the ROC curve for visual inspection.