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.
This exercise is part of the course
Introduction to Predictive Analytics in Python
Exercise instructions
- The model
logregfrom the last chapter has been created and fitted for you, the DataFrameXcontains 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 theroc_auc_scorefunction to calculate the AUC of the model.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Make predictions
predictions = logreg.____(____)
predictions_target = predictions[:,____]
# Calculate the AUC value
auc = ____(____, ____)
print(round(auc,2))