Bagging regressors

Bagging regressors are similar to bagging classifiers. They train each regressor model on a random subset of the original training set and aggregate the predictions. Then, the aggregation averages over the iterations because the target variable is numeric. In the following recipe, we are going to showcase the implementation of a bagging regressor with bootstrap samples.

Getting ready

We will import the required libraries, BaggingRegressor and DecisionTreeRegressor, from sklearn.ensemble and sklearn.tree respectively:

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

We read our dataset, which is bostonhousing.csv, and look at the dimensions of the DataFrame:

df_housingdata...