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.
Este ejercicio forma parte del curso
Marketing Analytics: Predicting Customer Churn in Python
Instrucciones del ejercicio
- Calculate the feature importances of
clf. - Use
plt.barh()to create a horizontal bar plot ofimportances.
Ejercicio interactivo práctico
Prueba este ejercicio completando el código de muestra.
# Calculate feature importances
importances = ____.____
# Create plot
____.____(range(X.shape[1]), ____)
plt.show()