πŸ“ƒ Solution for Exercise M6.01ΒΆ

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 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(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$")
Basic mean absolute error of the bagging regressor:
37.08 k$

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)
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
14 27 0.8 0.8 8 39.600154 0.796952 1
19 10 0.5 1.0 9 40.510696 0.901377 2
2 18 0.5 0.8 8 40.966039 1.083057 3
11 27 1.0 0.8 7 42.477936 1.241196 4
7 24 0.8 1.0 6 45.199883 1.157730 5
17 10 0.5 0.8 6 45.747758 0.940185 6
1 12 0.8 0.8 6 45.875778 1.426160 7
16 11 0.5 0.5 7 47.773700 1.831712 8
12 27 0.8 0.5 6 48.791431 1.193481 9
5 26 0.8 0.5 6 48.978393 0.493813 10
10 25 1.0 0.5 6 50.336881 1.422929 11
13 10 1.0 0.5 6 50.373324 2.772905 12
3 13 1.0 0.8 4 52.173540 0.998253 13
4 25 0.5 0.5 5 52.264665 0.885401 14
18 28 1.0 0.5 5 52.508655 1.217974 15
0 17 1.0 0.5 5 52.996889 0.867495 16
9 19 0.5 1.0 3 56.146980 0.958708 17
6 11 0.5 1.0 3 56.587621 1.284124 18
8 13 1.0 1.0 3 56.882876 0.918179 19
15 28 1.0 0.5 3 60.835457 1.098380 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$")
Mean absolute error after tuning of the bagging regressor:
42.20 k$

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