Feature importances
Although some candy attributes, such as chocolate, may be extremely popular, it doesn't mean they will be important to model prediction. After a random forest model has been fit, you can review the model's attribute, .feature_importances_
, to see which variables had the biggest impact. You can check how important each variable was in the model by looping over the feature importance array using enumerate()
.
If you are unfamiliar with Python's enumerate()
function, it can loop over a list while also creating an automatic counter.
This exercise is part of the course
Model Validation in Python
Exercise instructions
- Loop through the feature importance output of
rfr
. - Print the column names of
X_train
and the importance score for that column.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Fit the model using X and y
rfr.fit(X_train, y_train)
# Print how important each column is to the model
for i, item in enumerate(rfr.____):
# Use i and item to print out the feature importance of each column
print("{0:s}: {1:.2f}".format(X_train.columns[____], ____))