BaggingClassifier

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Explains estimator choice and sampling controls with a worked classifier, then applies bootstrapping to feature-selection stability.

As explained in Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

BaggingClassifier is a scikit-learn ensemble estimator that fits base classifiers on bootstrap samples and aggregates their predictions.

BaggingClassifier is a scikit-learn ensemble estimator that trains several classifiers on resampled data and combines their predictions. Set estimator to choose the base model, n_estimators to control ensemble size, and bootstrap, max_samples, and max_features to control sampling. section 5.1.1

Use the sampling parameters to express four related ensemble strategies:

Bagging samples training examples with replacement, using bootstrap=True, max_samples=1.0, and max_features=1.0. section 5.1.1
Pasting samples examples without replacement; random subspaces sample features; random patches sample both examples and features. section 5.1.1
Choose decision trees by default or provide another weak or strong classifier through estimator. section 5.1.1

Bagged tree-based classifier

from sklearn.ensemble import BaggingClassifier from sklearn.tree import DecisionTreeClassifier model = BaggingClassifier( estimator=DecisionTreeClassifier(), n_estimators=300, bootstrap=True, max_samples=1.0, max_features=1.0, random_state=0 ) A five-fold shuffled evaluation of the complete workflow reports 0.809 accuracy, with approximately 37.93 seconds for fitting and 0.83 seconds for prediction. section 5.1.1

BaggingClassifier also supports repeated logistic-regression fits for stability selection. Bootstrap resamples and L1-regularized coefficients reveal features that remain relevant across repeated models, making the procedure useful for reducing high-dimensional feature sets before prediction. section 6.2.1

Table 5.1 Bagging and sampling strategies (view table figure)

Averaging strategy	What happens to data	Parameters for BaggingClassifier/BaggingRegressor
Pasting	Training examples are sampled without replacement	`bootstrap = False` `max_samples < 1.0` `max_features = 1.0`
Bagging	Training examples are sampled with replacement (bootstrapping)	`bootstrap = True` `max_samples = 1.0` `max_features = 1.0`
Random subspaces	Features are sampled (without replacement)	`bootstrap = False` `max_samples = 1.0` `max_features < 1.0`
Random patches	Training examples and features are sampled without replacement	`bootstrap = False` `max_samples < 1.0` `max_features < 1.0`

Compare the parameter settings for bagging, pasting, random subspaces, and random patches. section 5.1.1

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Ensemble Methods for Machine LearningBaggingClassifier is a scikit-learn estimator class that trains an ensemble of base estimators on sampled subsets of data and aggregates their predictions.

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Related concepts

bootstrap samplingconfigured by · Bootstrap sampling is a resampling technique that repeatedly draws observations with replacement from observed data to estimate a statistic’s sampling behavior or variability.
out-of-bag erroruses · Out-of-bag error is an estimate of an ensemble’s prediction error computed for each observation using only ensemble members whose bootstrap samples omitted it.
stability selectionimplements · Stability selection is a feature-selection technique that evaluates how consistently features are selected across multiple resampled datasets.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI. The section links point to the parts of the book it draws on.