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.
Deze oefening maakt deel uit van de cursus
Marketing Analytics: Predicting Customer Churn in Python
Oefeninstructies
- Calculate the feature importances of
clf. - Use
plt.barh()to create a horizontal bar plot ofimportances.
Praktische interactieve oefening
Probeer deze oefening eens door deze voorbeeldcode in te vullen.
# Calculate feature importances
importances = ____.____
# Create plot
____.____(range(X.shape[1]), ____)
plt.show()