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: | *** <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 [[ | ** <code>[[sklearn.ensemble.ExtraTreesClassifier]]</code> An [[Ensemble Extra Trees Classifier]]. | ||
** <code>[[sklearn.ensemble.ExtraTreesRegressor]]</code> An [[ | ** <code>[[sklearn.ensemble.ExtraTreesRegressor]]</code> An [[Ensemble Extra Trees Regressor]]. | ||
** <code>[[sklearn.ensemble.GradientBoostingClassifier]]</code> [[Gradient Boosting | ** <code>[[sklearn.ensemble.GradientBoostingClassifier]]</code> [[Gradient Boosting Classifier]]. | ||
** <code>[[sklearn.ensemble.GradientBoostingRegressor]]</code> [[Gradient Boosting | ** <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 [[ | ** <code>[[sklearn.ensemble.RandomForestClassifier]]</code>A [[Random Forest Classifier]]. | ||
** <code>[[sklearn.ensemble.RandomForestRegressor]]</code> A [[ | ** <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 | ** <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> [[ | ** <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]]. | |||
---- | ---- | ||
---- | ---- | ||
== References == | == References == | ||
=== 2017 === | === 2017 === | ||
* (Scikit Learn, 2017) ⇒ http://scikit-learn.org/stable/modules/classes.html#module-sklearn.ensemble Retrieved:2017-10-22 | * (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 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. | ||
---- | ---- | ||
__NOTOC__ | __NOTOC__ | ||
[[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.
- 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.