Exercise

# Calculating AUC

The AUC value assesses how well a model can order observations from low probability to be target to high probability to be target. In Python, the `roc_auc_score`

function can be used to calculate the AUC of the model. It takes the true values of the target and the predictions as arguments.

You will make predictions again, before calculating its `roc_auc_score`

.

Instructions

**100 XP**

- The model
`logreg`

from the last chapter has been created and fitted for you, the dataframe`X`

contains the predictor columns of the basetable. Make predictions for the objects in the basetable. - Select the second column of
`predictions`

, as it contains the predictions for the target. - The true values of the target are loaded in
`y`

. Use the`roc_auc_score`

function to calculate the AUC of the model.