📝 Exercise M6.04#

The aim of the exercise is to get familiar with the histogram gradient-boosting in scikit-learn. Besides, we will use this model within a cross-validation framework in order to inspect internal parameters found via grid-search.

We will use the California housing dataset.

from sklearn.datasets import fetch_california_housing

data, target = fetch_california_housing(return_X_y=True, as_frame=True)
target *= 100  # rescale the target in k$

First, create a histogram gradient boosting regressor. You can set the trees number to be large, and configure the model to use early-stopping.

# Write your code here.

We will use a grid-search to find some optimal parameter for this model. In this grid-search, you should search for the following parameters:

  • max_depth: [3, 8];

  • max_leaf_nodes: [15, 31];

  • learning_rate: [0.1, 1].

Feel free to explore the space with additional values. Create the grid-search providing the previous gradient boosting instance as the model.

# Write your code here.

Finally, we will run our experiment through cross-validation. In this regard, define a 5-fold cross-validation. Besides, be sure to shuffle the data. Subsequently, use the function sklearn.model_selection.cross_validate to run the cross-validation. You should also set return_estimator=True, so that we can investigate the inner model trained via cross-validation.

# Write your code here.

Now that we got the cross-validation results, print out the mean and standard deviation score.

# Write your code here.

Then inspect the estimator entry of the results and check the best parameters values. Besides, check the number of trees used by the model.

# Write your code here.

Inspect the results of the inner CV for each estimator of the outer CV. Aggregate the mean test score for each parameter combination and make a box plot of these scores.

# Write your code here.