sklearn.ensemble Module

An sklearn.ensemble Module is an sklearn module that contains a collection of decision tree ensemble learning systems.

• Context:
  • It can (often) reference a sklearn.tree system.
    • sklearn.tree.Model_Name(self, arguments) or simply sklearn.tree.Model_Name()

      where DTName is the name of the selected decision tree ensemble learning system.

• Example(s)
  • sklearn.ensemble.AdaBoostClassifier An AdaBoost classifier.
  • sklearn.ensemble.AdaBoostRegressor An AdaBoost regressor.
  • sklearn.ensemble.BaggingClassifier A Bagging classifier.
  • sklearn.ensemble.BaggingRegressor A Bagging regressor.
  • sklearn.ensemble.ExtraTreesClassifier An Ensemble Extra Trees Classifier.
  • sklearn.ensemble.ExtraTreesRegressor An Ensemble Extra Trees Regressor.
  • sklearn.ensemble.GradientBoostingClassifier Gradient Boosting Classifier.
  • sklearn.ensemble.GradientBoostingRegressor Gradient Boosting Regressor.
  • sklearn.ensemble.IsolationForest Isolation Forest Algorithm.
  • sklearn.ensemble.RandomForestClassifierA Random Forest Classifier.
  • sklearn.ensemble.RandomForestRegressor A Random Forest Regressor.
  • sklearn.ensemble.RandomTreesEmbedding A Totally Random Trees Embedding System.
  • sklearn.ensemble.VotingClassifierSoft Voting/Majority Rule Classifier for unfitted estimators.
  • …
• Counter-Example(s):
  • sklearn.svm, a collection of Support Vector Machine algorithms.
  • sklearn.manifold, a collection of Manifold Learning Systems.
  • sklearn.tree, a collection of Decision Tree Learning Systems.
  • sklearn.metrics, a collection of Metrics Subroutines.
  • sklearn.covariance,a collection of Covariance Estimators.
  • sklearn.cluster.bicluster, a collection of Spectral Biclustering Algorithms.
  • sklearn.linear_model, a collection of Linear Model Regression Systems.
  • sklearn.neighbors, a collection of K Nearest Neighbors Algorithms.
  • sklearn.neural_network, a collection of Neural Network Systems.
• See: Decision Trees, Regression Task, Classification Task, Ensemble Learning, sklearn Boston Dataset-based Regression Trees Evaluation Task.

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References

2017

• (Scikit Learn, 2017) ⇒ http://scikit-learn.org/stable/modules/classes.html#module-sklearn.ensemble Retrieved:2017-10-22
  • QUOTE: The sklearn.ensemble module includes ensemble-based methods for classification, regression and anomaly detection.

    User guide: See the Ensemble methods section for further details.

    • ensemble.AdaBoostClassifier([…]) An AdaBoost classifier.
    • ensemble.AdaBoostRegressor([base_estimator, …]) An AdaBoost regressor.
    • ensemble.BaggingClassifier([base_estimator, …]) A Bagging classifier.
    • ensemble.BaggingRegressor([base_estimator, …]) A Bagging regressor.
    • ensemble.ExtraTreesClassifier([…]) An extra-trees classifier.
    • ensemble.ExtraTreesRegressor([n_estimators, …]) An extra-trees regressor.
    • ensemble.GradientBoostingClassifier([loss, …]) Gradient Boosting for classification.
    • ensemble.GradientBoostingRegressor([loss, …]) Gradient Boosting for regression.
    • ensemble.IsolationForest([n_estimators, …]) Isolation Forest Algorithm.
    • ensemble.RandomForestClassifier([…])A random forest classifier.
    • ensemble.RandomForestRegressor([…]) A random forest regressor.
    • ensemble.RandomTreesEmbedding([…]) An ensemble of totally random trees.
    • ensemble.VotingClassifier(estimators[, …])Soft Voting/Majority Rule classifier for unfitted estimators.

      partial dependence

       Partial dependence plots for tree ensembles.

    • ensemble.partial_dependence.partial_dependence(…), Partial dependence of target_variables.
    • ensemble.partial_dependence.plot_partial_dependence(…), Partial dependence plots for features.

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