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  3. Ensemble Methods in Python

Exercise

Your first AdaBoost model

In the previous lesson you built models to predict the log-revenue of movies. You started with a simple linear regression and got an RMSE of 7.34. Then, you tried to improve it with an iteration of boosting, getting to a lower RMSE of 7.28.

In this exercise, you'll build your first AdaBoost model - an AdaBoostRegressor - in an attempt to improve performance even further.

The movies dataset has been loaded and split into train and test sets. Here you'll be using the 'budget' and 'popularity' features, which were already standardized for you using StandardScaler() from sklearn.preprocessing module.

Instructions

100 XP
  • Instantiate the default linear regression model.
  • Build and fit an AdaBoostRegressor, using the linear regression as the base model and 12 estimators.
  • Calculate the predictions on the test set.