Visualizing feature importances
Your random forest classifier from earlier exercises has been fit to the telco data and is available to you as clf. Let's visualize the feature importances and get a sense for what the drivers of churn are, using matplotlib's barh to create a horizontal bar plot of feature importances.
Cet exercice fait partie du cours
Marketing Analytics: Predicting Customer Churn in Python
Instructions
- Calculate the feature importances of
clf. - Use
plt.barh()to create a horizontal bar plot ofimportances.
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Calculate feature importances
importances = ____.____
# Create plot
____.____(range(X.shape[1]), ____)
plt.show()