π 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$"
)
Basic mean absolute error of the bagging regressor:
36.73 k$
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 regressorβs 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 | |
---|---|---|---|---|---|---|
8 | 10 | 1.0 | 0.8 | 9 | 38.550679 | 1.809574 |
15 | 27 | 0.8 | 0.8 | 9 | 38.907349 | 1.036379 |
12 | 18 | 0.8 | 0.8 | 9 | 39.911581 | 1.687790 |
7 | 25 | 1.0 | 1.0 | 8 | 40.896636 | 1.129437 |
14 | 12 | 1.0 | 1.0 | 8 | 41.200185 | 1.150336 |
13 | 20 | 1.0 | 1.0 | 7 | 43.147905 | 1.172360 |
16 | 13 | 0.8 | 1.0 | 6 | 45.294455 | 1.468181 |
2 | 27 | 0.5 | 0.5 | 8 | 45.975905 | 0.882917 |
6 | 10 | 0.5 | 0.5 | 7 | 47.078127 | 2.479784 |
5 | 10 | 0.5 | 0.5 | 9 | 47.633190 | 2.922904 |
18 | 26 | 1.0 | 1.0 | 5 | 48.029047 | 1.292306 |
3 | 10 | 0.8 | 0.8 | 5 | 48.348323 | 1.239787 |
11 | 17 | 0.8 | 0.5 | 6 | 48.688475 | 1.567236 |
9 | 19 | 0.5 | 0.5 | 6 | 50.056939 | 2.435231 |
1 | 25 | 1.0 | 0.5 | 6 | 50.634609 | 1.467715 |
0 | 20 | 0.8 | 0.5 | 6 | 50.815898 | 2.384356 |
10 | 25 | 0.5 | 1.0 | 4 | 51.465324 | 1.168005 |
19 | 13 | 0.8 | 0.5 | 5 | 53.169198 | 2.596839 |
17 | 15 | 1.0 | 0.8 | 3 | 57.893897 | 1.250442 |
4 | 20 | 1.0 | 0.5 | 3 | 61.187502 | 1.228751 |
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$"
)
Mean absolute error after tuning of the bagging regressor:
39.99 k$
We see that the predictor provided by the bagging regressor does not need much hyperparameter tuning compared to a single decision tree.