sklearn.ensemble Module: Difference between revisions

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* <B>Context:</B>
* <B>Context:</B>
** It can (often) reference a [[sklearn.tree]] system.
** It can (often) reference a [[sklearn.tree]] system.
*** <code>[[sklearn.tree]].<span style="font-weight:italic; color:blue">DTEName(self, arguments)</i></code>  or simply <code>[[sklearn.tree]].<span style="font-weight:italic; color:blue">DTEName()</i></code> <P>where <i>DTName</i> is the name of the selected [[decision tree ensemble learning system]].
*** <code>[[sklearn.tree]].<span style="font-weight:italic; color:green">Model_Name(self, arguments)</i></code>  or simply <code>[[sklearn.tree]].<span style="font-weight:italic; color:green">Model_Name()</i></code>         <P>         where <i>DTName</i> is the name of the selected [[decision tree ensemble learning system]].
* <B>Example(s)</B>
* <B>Example(s)</B>
** <code>[[sklearn.ensemble.AdaBoostClassifier]]</code> An [[AdaBoost classifier]].
** <code>[[sklearn.ensemble.AdaBoostClassifier]]</code> An [[AdaBoost classifier]].
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** <code>[[sklearn.ensemble.BaggingClassifier]]</code> A [[Bagging classifier]].
** <code>[[sklearn.ensemble.BaggingClassifier]]</code> A [[Bagging classifier]].
** <code>[[sklearn.ensemble.BaggingRegressor]]</code> A [[Bagging regressor]].
** <code>[[sklearn.ensemble.BaggingRegressor]]</code> A [[Bagging regressor]].
** <code>[[sklearn.ensemble.ExtraTreesClassifier]]</code> An [[extra-trees classifier]].
** <code>[[sklearn.ensemble.ExtraTreesClassifier]]</code> An [[Ensemble Extra Trees Classifier]].
** <code>[[sklearn.ensemble.ExtraTreesRegressor]]</code> An [[extra-trees regressor]].
** <code>[[sklearn.ensemble.ExtraTreesRegressor]]</code> An [[Ensemble Extra Trees Regressor]].
** <code>[[sklearn.ensemble.GradientBoostingClassifier]]</code> [[Gradient Boosting for classification]].
** <code>[[sklearn.ensemble.GradientBoostingClassifier]]</code> [[Gradient Boosting Classifier]].
** <code>[[sklearn.ensemble.GradientBoostingRegressor]]</code> [[Gradient Boosting for regression]].
** <code>[[sklearn.ensemble.GradientBoostingRegressor]]</code> [[Gradient Boosting Regressor]].
** <code>[[sklearn.ensemble.IsolationForest]]</code> [[Isolation Forest Algorithm]]
** <code>[[sklearn.ensemble.IsolationForest]]</code> [[Isolation Forest Algorithm]].
** <code>[[sklearn.ensemble.RandomForestClassifier]]</code>A [[random forest classifier]].
** <code>[[sklearn.ensemble.RandomForestClassifier]]</code>A [[Random Forest Classifier]].
** <code>[[sklearn.ensemble.RandomForestRegressor]]</code> A [[random forest regressor]].
** <code>[[sklearn.ensemble.RandomForestRegressor]]</code> A [[Random Forest Regressor]].
** <code>[[sklearn.ensemble.RandomTreesEmbedding]]</code> A [[Totally Random Trees Embedding System]].
** <code>[[sklearn.ensemble.RandomTreesEmbedding]]</code> A [[Totally Random Trees Embedding System]].
** <code>[[sklearn.ensemble.VotingClassifier]]</code>[[Soft Voting]]/[[Majority Rule classifier]] for unfitted estimators.
** <code>[[sklearn.ensemble.VotingClassifier]]</code>[[Soft Voting]]/[[Majority Rule Classifier]] for unfitted estimators.
** …
* <B>Counter-Example(s):</B>
* <B>Counter-Example(s):</B>
** [[sklearn.linear_model]], [[sklearn.neighbors]].
** <code>[[sklearn.svm]]</code>, a collection of [[Support Vector Machine]] algorithms.
* <B>See:</B> [[DTree System]].
** <code>[[sklearn.manifold]]</code>, a collection of [[Manifold Learning System]]s.
** <code>[[sklearn.tree]]</code>, a collection of [[Decision Tree Learning System]]s.
** <code>[[sklearn.metrics]]</code>, a collection of [[Metric]]s [[Subroutine]]s.
** <code>[[sklearn.covariance]]</code>,a collection of [[Covariance Estimator]]s.
** <code>[[sklearn.cluster.bicluster]]</code>, a collection of [[Spectral Biclustering Algorithm]]s.
** <code>[[sklearn.linear_model]]</code>, a collection of [[Linear Model Regression System]]s.
** <code>[[sklearn.neighbors]]</code>, a collection of [[K Nearest Neighbors Algorithm]]s.
** <code>[[sklearn.neural_network]]</code>, a collection of [[Neural Network System]]s.
* <B>See:</B> [[Decision Trees]], [[Regression Task]], [[Classification Task]], [[Ensemble Learning]], [[sklearn Boston Dataset-based Regression Trees Evaluation Task]].
 
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== References ==
== References ==


=== 2017 ===
=== 2017 ===
* (Scikit Learn, 2017) &rArr; http://scikit-learn.org/stable/modules/classes.html#module-sklearn.ensemble Retrieved:2017-10-22
* (Scikit Learn, 2017) &rArr; http://scikit-learn.org/stable/modules/classes.html#module-sklearn.ensemble Retrieved:2017-10-22
** QUOTE: The [[sklearn.ensemble module]] includes [[ensemble-based method]]s for [[classification]], [[regression]] and [[anomaly detection]]. <P> User guide: See the [[Ensemble method]]s section for further details.
** QUOTE: The [[sklearn.ensemble Module|sklearn.ensemble module]] includes [[ensemble-based method]]s for [[classification]], [[regression]] and [[anomaly detection]].       <P>         User guide: See the [[Ensemble method]]s section for further details.
*** <code>ensemble.AdaBoostClassifier([…])</code> An [[AdaBoost classifier]].
*** <code>ensemble.AdaBoostClassifier([…])</code> An [[AdaBoost classifier]].
*** <code>ensemble.AdaBoostRegressor([base_estimator, …])</code> An [[AdaBoost regressor]].
*** <code>ensemble.AdaBoostRegressor([base_estimator, …])</code> An [[AdaBoost regressor]].
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*** <code>ensemble.GradientBoostingClassifier([loss, …])</code> [[Gradient Boosting for classification]].
*** <code>ensemble.GradientBoostingClassifier([loss, …])</code> [[Gradient Boosting for classification]].
*** <code>ensemble.GradientBoostingRegressor([loss, …])</code> [[Gradient Boosting for regression]].
*** <code>ensemble.GradientBoostingRegressor([loss, …])</code> [[Gradient Boosting for regression]].
*** <code>ensemble.IsolationForest([n_estimators, …])</code> [[Isolation Forest Algorithm]]
*** <code>ensemble.IsolationForest([n_estimators, …])</code> [[Isolation Forest Algorithm]].
*** <code>ensemble.RandomForestClassifier([…])</code>A [[random forest classifier]].
*** <code>ensemble.RandomForestClassifier([…])</code>A [[random forest classifier]].
*** <code>ensemble.RandomForestRegressor([…])</code> A [[random forest regressor]].
*** <code>ensemble.RandomForestRegressor([…])</code> A [[random forest regressor]].
*** <code>ensemble.RandomTreesEmbedding([…])</code> An [[ensemble of totally random trees]].
*** <code>ensemble.RandomTreesEmbedding([…])</code> An [[ensemble of totally random trees]].
*** <code>ensemble.VotingClassifier(estimators[, …])</code>[[Soft Voting]]/[[Majority Rule classifier]] for unfitted estimators.<P><B>partial dependence</B><P>[[Partial dependence plot]]s for [[tree ensemble]]s.
*** <code>ensemble.VotingClassifier(estimators[, …])</code>[[Soft Voting]]/[[Majority Rule classifier]] for unfitted estimators.         <P><B>partial dependence</B>         <P>         [[Partial dependence plot]]s for [[tree ensemble]]s.
*** <code>ensemble.partial_dependence.partial_dependence(…)</code>, [[Partial dependence]] of [[target_variable]]s.
*** <code>ensemble.partial_dependence.partial_dependence(…)</code>, [[Partial dependence]] of [[target_variable]]s.
*** <code>ensemble.partial_dependence.plot_partial_dependence(…)</code>, [[Partial dependence]] plots for [[feature]]s.
*** <code>ensemble.partial_dependence.plot_partial_dependence(…)</code>, [[Partial dependence]] plots for [[feature]]s.


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[[Category:Concept]]
[[Category:Concept]]

Latest revision as of 17:08, 1 June 2024

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



References

2017