📃 Solution for Exercise M6.01

The aim of this notebook is to investigate if we can tune the hyperparameters of a bagging regressor and evaluate the gain obtained.

We will load the California housing dataset and split it into a training and a testing set.

from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split

data, target = fetch_california_housing(as_frame=True, return_X_y=True)
target *= 100  # rescale the target in k$
data_train, data_test, target_train, target_test = train_test_split(
    data, target, random_state=0, test_size=0.5
)

Note

If you want a deeper overview regarding this dataset, you can refer to the Appendix - Datasets description section at the end of this MOOC.

Create a BaggingRegressor and provide a DecisionTreeRegressor to its parameter estimator. Train the regressor and evaluate its generalization performance on the testing set using the mean absolute error.

# solution
from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import BaggingRegressor

tree = DecisionTreeRegressor()
bagging = BaggingRegressor(estimator=tree, n_jobs=2)
bagging.fit(data_train, target_train)
target_predicted = bagging.predict(data_test)
print(
    "Basic mean absolute error of the bagging regressor:\n"
    f"{mean_absolute_error(target_test, target_predicted):.2f} k$"
)

Basic mean absolute error of the bagging regressor:
36.95 k$

Now, create a RandomizedSearchCV instance using the previous model and tune the important parameters of the bagging regressor. Find the best parameters and check if you are able to find a set of parameters that improve the default regressor still using the mean absolute error as a metric.

Tip

You can list the bagging regressor’s parameters using the get_params method.

# solution
for param in bagging.get_params().keys():
    print(param)

bootstrap
bootstrap_features
estimator__ccp_alpha
estimator__criterion
estimator__max_depth
estimator__max_features
estimator__max_leaf_nodes
estimator__min_impurity_decrease
estimator__min_samples_leaf
estimator__min_samples_split
estimator__min_weight_fraction_leaf
estimator__monotonic_cst
estimator__random_state
estimator__splitter
estimator
max_features
max_samples
n_estimators
n_jobs
oob_score
random_state
verbose
warm_start

from scipy.stats import randint
from sklearn.model_selection import RandomizedSearchCV

param_grid = {
    "n_estimators": randint(10, 30),
    "max_samples": [0.5, 0.8, 1.0],
    "max_features": [0.5, 0.8, 1.0],
    "estimator__max_depth": randint(3, 10),
}
search = RandomizedSearchCV(
    bagging, param_grid, n_iter=20, scoring="neg_mean_absolute_error"
)
_ = search.fit(data_train, target_train)

import pandas as pd

columns = [f"param_{name}" for name in param_grid.keys()]
columns += ["mean_test_error", "std_test_error"]
cv_results = pd.DataFrame(search.cv_results_)
cv_results["mean_test_error"] = -cv_results["mean_test_score"]
cv_results["std_test_error"] = cv_results["std_test_score"]
cv_results[columns].sort_values(by="mean_test_error")

	param_n_estimators	param_max_samples	param_max_features	param_estimator__max_depth	mean_test_error	std_test_error
13	12	0.5	0.8	9	39.254001	0.715794
8	13	0.5	0.8	9	40.162642	2.136100
18	18	0.5	0.8	8	40.557564	1.110431
0	26	0.5	0.8	8	41.021213	1.293390
9	12	0.5	0.8	8	41.936085	1.358879
4	10	0.8	0.8	8	42.043888	1.911288
6	11	0.5	1.0	5	48.112856	1.094527
1	13	0.8	1.0	5	48.264512	1.120409
7	17	1.0	0.8	5	48.352574	1.402634
10	28	0.8	0.8	5	48.361833	1.354517
16	22	0.5	0.5	7	48.478518	2.038750
11	13	0.5	0.5	7	48.838121	0.556691
17	19	0.8	0.5	6	49.037088	0.952953
19	24	0.5	0.5	6	50.915474	1.563177
12	25	0.5	1.0	4	51.359034	0.907383
14	28	0.5	0.8	4	52.129905	1.221536
5	20	0.8	0.8	4	52.636009	0.929005
3	14	1.0	0.8	4	52.814996	0.841228
15	28	0.5	0.5	4	55.914924	2.241993
2	25	1.0	0.8	3	58.042177	1.261777

target_predicted = search.predict(data_test)
print(
    "Mean absolute error after tuning of the bagging regressor:\n"
    f"{mean_absolute_error(target_test, target_predicted):.2f} k$"
)

Mean absolute error after tuning of the bagging regressor:
40.19 k$

We see that the predictor provided by the bagging regressor does not need much hyperparameter tuning compared to a single decision tree.