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  3. Feature Engineering with PySpark

Exercise

Building a Regression Model

One of the great things about PySpark ML module is that most algorithms can be tried and tested without changing much code. Random Forest Regression is a fairly simple ensemble model, using bagging to fit. Another tree based ensemble model is Gradient Boosted Trees which uses a different approach called boosting to fit. In this exercise let's train a GBTRegressor.

Instructions

100 XP
  • Import GBTRegressor from pyspark.ml.regression which you will notice is the same module as RandomForestRegressor.
  • Instantiate GBTRegressor with featuresCol set to the vector column of our features named, features, labelCol set to our dependent variable, SALESCLOSEPRICE and the random seed to 42
  • Train the model by calling fit() on gbt with the imported training data, train_df.