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.

Cet exercice fait partie du cours

Credit Risk Modeling in Python

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Instructions

Create a histogram of the predicted probabilities clf_gbt_preds.
Calculate the threshold for an acceptance rate of 85% using quantile(). Store this value as threshold.
Plot the histogram again, except this time add a reference line using .axvline().

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# 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.____()

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