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Define the GB regressor

You'll now revisit the Bike Sharing Demand dataset that was introduced in the previous chapter. Recall that your task is to predict the bike rental demand using historical weather data from the Capital Bikeshare program in Washington, D.C.. For this purpose, you'll be using a gradient boosting regressor.

As a first step, you'll start by instantiating a gradient boosting regressor which you will train in the next exercise.

This exercise is part of the course

Machine Learning with Tree-Based Models in Python

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Exercise instructions

  • Import GradientBoostingRegressor from sklearn.ensemble.

  • Instantiate a gradient boosting regressor by setting the parameters:

    • max_depth to 4

    • n_estimators to 200

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import GradientBoostingRegressor
____

# Instantiate gb
gb = ____(____=____, 
            ____=____,
            random_state=2)
Edit and Run Code