Difference between revisions of "sklearn.tree Module"

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* <B>Context:</B>
 
* <B>Context:</B>
 
** It require to call/select a [[Decision Tree Learning System]] :
 
** It require to call/select a [[Decision Tree 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>Model_Name</i> is the name of the selected [[decision-tree learning system]].
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*** <code>[[sklearn.tree Module|sklearn.tree]].<span style="font-weight:italic; color:Green">Model_Name(self, arguments)</i></code>  or simply <code>[[sklearn.tree Module|sklearn.tree]].<span style="font-weight:italic; color:Green">Model_Name()</i></code> <P>where <i>Model_Name</i> is the name of the selected [[decision-tree learning system]].
 
* <B>Example(s)</B>
 
* <B>Example(s)</B>
 
** <code>[[sklearn.tree.DecisionTreeClassifier]]()</code>, a [[Classification Tree Learning System]].
 
** <code>[[sklearn.tree.DecisionTreeClassifier]]()</code>, a [[Classification Tree Learning System]].

Revision as of 20:45, 23 December 2019

An sklearn.tree Module is an sklearn module of decision-tree learning systems.



References

2017

User guide: See the Decision Trees section for further details.
tree.DecisionTreeClassifier([criterion, …]) 	A decision tree classifier.
tree.DecisionTreeRegressor([criterion, …]) 	A decision tree regressor.
tree.ExtraTreeClassifier([criterion, …]) 	An extremely randomized tree classifier.
tree.ExtraTreeRegressor([criterion, …]) 	An extremely randomized tree regressor.
tree.export_graphviz(decision_tree[, …]) 	Export a decision tree in DOT format.