Constructing the lift curve

The lift curve is an evaluation curve that assesses the performance of your model. It shows how many times more than average the model reaches targets.

To construct this curve, you can use the plot_lift_curve method in the scikitplot module and the matplotlib.pyplot module. As for each model evaluation metric or curve, you need the true target values on the one hand and the predictions on the other hand to construct the cumulative gains curve.

This is a part of the course

“Introduction to Predictive Analytics in Python”

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Exercise instructions

Import the matplotlib.pyplot module.
Import the scikitplot module.
Construct the cumulative gains curve, given that the model outputs the values in predictions_test and the true target values are in targets_test.

Hands-on interactive exercise

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

# Import the matplotlib.pyplot module 
____ as plt

# Import the scikitplot module
____ as skplt

# Plot the lift curve
skplt.metrics.____(____, ____)
plt.show()

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