Calculating ROC Curves and AUC

The previous exercises have demonstrated that accuracy is a very misleading measure of model performance on imbalanced datasets. Graphing the model's performance better illustrates the tradeoff between a model that is overly aggressive and one that is overly passive.

In this exercise you will create a ROC curve and compute the area under the curve (AUC) to evaluate the logistic regression model of donations you built earlier.

The dataset donors with the column of predicted probabilities, donation_prob, has been loaded for you.

This exercise is part of the course

Supervised Learning in R: Classification

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Exercise instructions

Load the pROC package.
Create a ROC curve with roc() and the columns of actual and predicted donations. Store the result as ROC.
Use plot() to draw the ROC object. Specify col = "blue" to color the curve blue.
Compute the area under the curve with auc().

Hands-on interactive exercise

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

# Load the pROC package


# Create a ROC curve
ROC <- roc(___, ___)

# Plot the ROC curve
plot(___, col = ___)

# Calculate the area under the curve (AUC)
auc(___)

Edit and Run Code