Delayed flights with Gradient-Boosted Trees
You've previously built a classifier for flights likely to be delayed using a Decision Tree. In this exercise you'll compare a Decision Tree model to a Gradient-Boosted Trees model.
The flights data have been randomly split into flights_train and flights_test.
Deze oefening maakt deel uit van de cursus
Machine Learning with PySpark
Oefeninstructies
- Import the classes required to create Decision Tree and Gradient-Boosted Tree classifiers.
- Create Decision Tree and Gradient-Boosted Tree classifiers. Train on the training data.
- Create an evaluator and calculate AUC on testing data for both classifiers. Which model performs better?
- For the Gradient-Boosted Tree classifier print the number of trees and the relative importance of features.
Praktische interactieve oefening
Probeer deze oefening eens door deze voorbeeldcode in te vullen.
# Import the classes required
from pyspark.ml.____ import ____, ____
from pyspark.ml.evaluation import BinaryClassificationEvaluator
# Create model objects and train on training data
tree = ____().____(____)
gbt = ____().____(____)
# Compare AUC on testing data
evaluator = ____()
print(evaluator.____(tree.____(____)))
print(evaluator.____(gbt.____(____)))
# Find the number of trees and the relative importance of features
print(gbt.____)
print(gbt.____)