sklearn.ensemble Module
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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 simplysklearn.tree.Model_Name()
where DTName is the name of the selected decision tree ensemble learning system.
- It can (often) reference a sklearn.tree 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.RandomForestClassifier
A Random Forest Classifier.sklearn.ensemble.RandomForestRegressor
A Random Forest Regressor.sklearn.ensemble.RandomTreesEmbedding
A Totally Random Trees Embedding System.sklearn.ensemble.VotingClassifier
Soft 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.
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
ensemble.partial_dependence.partial_dependence(…)
, Partial dependence of target_variables.ensemble.partial_dependence.plot_partial_dependence(…)
, Partial dependence plots for features.
- QUOTE: The sklearn.ensemble module includes ensemble-based methods for classification, regression and anomaly detection.