📃 Solution of 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 base_estimator. Train the regressor and evaluate its statistical performance on the testing set using the mean absolute error.

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

tree = DecisionTreeRegressor()
bagging = BaggingRegressor(base_estimator=tree, n_jobs=2)
bagging.fit(data_train, target_train)
target_predicted = bagging.predict(data_test)
print(f"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:
36.38 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.

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

base_estimator__ccp_alpha
base_estimator__criterion
base_estimator__max_depth
base_estimator__max_features
base_estimator__max_leaf_nodes
base_estimator__min_impurity_decrease
base_estimator__min_impurity_split
base_estimator__min_samples_leaf
base_estimator__min_samples_split
base_estimator__min_weight_fraction_leaf
base_estimator__random_state
base_estimator__splitter
base_estimator
bootstrap
bootstrap_features
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],
    "base_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_score", "std_test_score", "rank_test_score"]
cv_results = pd.DataFrame(search.cv_results_)
cv_results = cv_results[columns].sort_values(by="rank_test_score")
cv_results["mean_test_score"] = -cv_results["mean_test_score"]
cv_results

	param_n_estimators	param_max_samples	param_max_features	param_base_estimator__max_depth	mean_test_score	std_test_score	rank_test_score
15	21	1.0	0.8	8	40.030884	1.273278	1
1	14	0.5	0.8	8	41.353784	1.116039	2
6	21	1.0	0.8	7	41.799669	1.521985	3
17	22	0.8	0.8	7	42.216457	1.072362	4
12	21	0.5	0.8	7	42.388688	1.177851	5
19	25	1.0	1.0	7	42.746657	1.276234	6
18	15	0.5	1.0	7	43.014306	1.303823	7
4	24	1.0	1.0	7	43.048924	0.969746	8
2	14	0.5	1.0	7	43.272181	1.227020	9
7	17	0.5	1.0	6	45.258098	1.229740	10
11	15	1.0	1.0	6	45.467947	1.198316	11
14	14	0.5	0.5	7	46.188176	1.689583	12
16	11	0.5	0.8	5	48.486368	1.007336	13
10	11	1.0	0.8	5	49.332554	1.022812	14
13	24	1.0	1.0	4	51.860316	1.123646	15
8	29	0.5	0.8	4	52.360165	1.393773	16
3	15	0.5	0.5	5	52.491499	2.411352	17
0	10	0.5	1.0	3	56.769980	1.116707	18
5	27	1.0	0.8	3	57.312467	1.262450	19
9	14	0.5	0.8	3	58.037170	0.844139	20

target_predicted = search.predict(data_test)
print(f"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:
40.66 k$

We see that the predictor provided by the bagging regressor does not need much hyperparameter tuning compared to a single decision tree. We see that the bagging regressor provides a predictor for which tuning the hyperparameters is not as important as in the case of fitting a single decision tree.

Scikit-learn course

📃 Solution of Exercise M6.01¶