Visualizing quantiles of acceptance
You know how quantile() works to compute a threshold, and you've seen an example of what it does to split the loans into accepted and rejected. What does this threshold look like for the test set, and how can you visualize it?
To check this, you can create a histogram of the probabilities and add a reference line for the threshold. With this, you can visually show where the threshold exists in the distribution.
The model predictions clf_gbt_preds have been loaded into the workspace.
Diese Übung ist Teil des Kurses
Credit Risk Modeling in Python
Anleitung zur Übung
- Create a histogram of the predicted probabilities
clf_gbt_preds. - Calculate the threshold for an acceptance rate of 85% using
quantile(). Store this value asthreshold. - Plot the histogram again, except this time add a reference line using
.axvline().
Interaktive Übung
Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.
# Plot the predicted probabilities of default
plt.____(____, color = 'blue', bins = 40)
# Calculate the threshold with quantile
____ = np.____(____, ____)
# Add a reference line to the plot for the threshold
plt.____(x = ____, color = 'red')
plt.____()