πŸ“ƒ Solution for Exercise M6.01

πŸ“ƒ 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$"
)
Basic mean absolute error of the bagging regressor:
36.97 k$

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
18 17 0.8 1.0 9 39.395211 1.254508
15 13 0.8 1.0 9 39.613209 1.363516
10 28 0.5 1.0 9 39.650733 0.848104
19 20 0.5 1.0 9 39.751091 0.949682
5 28 1.0 1.0 8 40.930693 1.048135
7 26 0.8 0.8 7 42.889283 0.975648
3 27 1.0 1.0 7 43.177506 0.883662
1 16 0.8 1.0 6 45.531471 1.509217
9 24 0.8 0.8 6 45.644531 1.086302
0 17 0.5 0.5 9 45.668266 2.800600
14 26 0.5 1.0 5 47.846061 1.223634
16 21 0.8 0.5 6 49.254316 1.215204
17 13 0.5 0.5 7 49.289557 3.930420
11 28 0.5 1.0 4 51.338870 1.089747
4 28 1.0 1.0 4 51.742919 1.225406
6 10 0.8 1.0 4 51.752229 1.176078
8 14 0.5 0.8 4 52.150449 1.282377
2 13 0.5 0.5 4 55.605738 1.788238
12 15 0.5 1.0 3 55.979047 0.894541
13 13 1.0 0.5 4 56.822999 1.926333
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$"
)
Mean absolute error after tuning of the bagging regressor:
39.40 k$

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