Constructing the cumulative gains curve
The cumulative gains curve is an evaluation curve that assesses the performance of your model. It shows the percentage of targets reached when considering a certain percentage of your population with the highest probability to be target according to your model.
To construct this curve, you can use the .plot_cumulative_gain()
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”
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 intargets_test
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Import the matplotlib.pyplot module
import ____.____ as plt
# Import the scikitplot module
import ____ as skplt
# Plot the cumulative gains graph
skplt.metrics.____(targets_test, ____)
plt.show()