Exercise

# 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 agressive 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`

,is already loaded in your workspace.

Instructions

**100 XP**

- 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()`

.