Get startedGet started for free

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.

This exercise is part of the course

Machine Learning with caret in R

View Course

Exercise instructions

model, test, and train from the last exercise using the sonar data are loaded in your workspace.

  • Predict probabilities (i.e. type = "response") on the test set, then store the result as p.
  • Make an ROC curve using the predicted test set probabilities.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Predict on test: p


# Make ROC curve
Edit and Run Code