These models focus on combining multiple models to improve generalizability and robustness.

Regressors

Model Name	Summary	Link to sklearn-documentation
AdaBoostRegressor	An adaptive boosting algorithm that fits a sequence of weak learners, adjusting the weights of data points based on previous errors to focus on “hard” cases.	Documentation
BaggingRegressor	Fits multiple versions of a base regressor on different random subsets of the data and averages their predictions to reduce variance.	Documentation
ExtraTreesRegressor	”Extremely Randomized Trees” take randomness a step further than Random Forests by choosing split points totally at random for each feature, often reducing variance further.	Documentation
GradientBoostingRegressor	An additive model that builds trees one at a time, where each new tree helps to correct the errors (residuals) made by the previously built trees.	Documentation
HistGradientBoostingRegressor	A highly optimized version of Gradient Boosting that bins continuous input features into integer-valued bins, significantly speeding up training on large datasets ( $n > 10, 000$ ).	Documentation
RandomForestRegressor	A staple of machine learning that fits a forest of decision trees on various sub-samples of the dataset and averages them to control over-fitting.	Documentation

Classifiers

Model Name	Summary	Link to Documentation
AdaBoostClassifier	A meta-estimator that begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the same dataset, where weights of incorrectly classified instances are adjusted.	Documentation
BaggingClassifier	Fits base classifiers each on random subsets of the original dataset and then aggregates their individual predictions to form a final prediction.	Documentation
ExtraTreesClassifier	Implements a meta estimator that fits a number of randomized decision trees (a.k.a. extra-trees) on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.	Documentation
GradientBoostingClassifier	Builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions.	Documentation
HistGradientBoostingClassifier	A much faster variant of Gradient Boosting for large datasets (n > 10,000), which bins continuous input features into discrete integer-valued bins.	Documentation
RandomForestClassifier	A meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.	Documentation