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.
Diese Übung ist Teil des Kurses
<Kurs>Marketing Analytics: Predicting Customer Churn in Python</Kurs>Übungsanweisungen
- Calculate the feature importances of
clf. - Use
plt.barh()to create a horizontal bar plot ofimportances.
Interaktive praktische Übung
Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.
# Calculate feature importances
importances = ____.____
# Create plot
____.____(range(X.shape[1]), ____)
plt.show()