Hi Sebastian, Thanks for your answer.
I dont't use the iris dataset. My classes are distributed in my Y array. It seems that I can get the classes in alphabetical order with > clf.classes_ where clf is my tree. And with > export_graphviz(clf, > out_file=dot_data,feature_names=FEATURES,class_names=clf.classes_) the nodes of the graphical tree seem to be filled with the predominant class and samples repartition in a vector with the classes in alphabetical order ( the same order as in clf.classes_) I have to confirm that with more classes. Regards Gregory ________________________________ De : scikit-learn <[email protected]> de la part de Sebastian Raschka <[email protected]> Envoyé : lundi 24 octobre 2016 17:47 À : Scikit-learn user and developer mailing list Objet : Re: [scikit-learn] tree visualization with class names in leaves Hi, Greg, if you provide the `class_names` argument, a “class” label of the majority class will be added at the bottom of each node. For instance, if you have the Iris dataset, with class labels 0, 1, 2, you can provide the `class_names` as ['setosa', 'versicolor', 'virginica’], where 0 -> ‘setosa’, 1 -> ‘versicolor’, 2 -> ‘virginica’. Best, Sebastian > On Oct 24, 2016, at 10:18 AM, greg g <[email protected]> wrote: > > bLaf1ox-forefront-antispam-report: EFV:NLI; SFV:NSPM; > SFS:(10019020)(98900003); > DIR:OUT; SFP:1102; SCL:1; SRVR:DB5EUR03HT168; > H:DB3PR04MB0780.eurprd04.prod.outlook.com; FPR:; SPF:None; LANG:en; > x-ms-office365-filtering-correlation-id: 319900b9-973c-49bb-8e9a-08d3fc1895c4 > x-microsoft-antispam: UriScan:; BCL:0; PCL:0; > RULEID:(1601124038)(1603103081)(1601125047); SRVR:DB5EUR03HT168; > x-exchange-antispam-report-cfa-test: BCL:0; PCL:0; > RULEID:(432015012)(82015046); SRVR:DB5EUR03HT168; BCL:0; PCL:0; RULEID:; > SRVR:DB5EUR03HT168; > x-forefront-prvs: 0105DAA385 > X-OriginatorOrg: outlook.com > X-MS-Exchange-CrossTenant-originalarrivaltime: 24 Oct 2016 14:18:11.0102 (UTC) > X-MS-Exchange-CrossTenant-fromentityheader: Internet > X-MS-Exchange-CrossTenant-id: 84df9e7f-e9f6-40af-b435-aaaaaaaaaaaa > X-MS-Exchange-Transport-CrossTenantHeadersStamped: DB5EUR03HT168 > > > Hi, > I just begin with scikit-learn and would like to visualize a classification > tree with class names displayed in the leaves as shown in the > SCIKITLEARN.TREE documentation > http://scikit-learn.org/stable/modules/tree.html where we find > class=’virginica’ etc… [http://scikit-learn.org/stable/_images/iris.svg]<http://scikit-learn.org/stable/modules/tree.html> 1.10. Decision Trees — scikit-learn 0.18 documentation<http://scikit-learn.org/stable/modules/tree.html> scikit-learn.org Decision-tree learners can create over-complex trees that do not generalise the data well. This is called overfitting. Mechanisms such as pruning (not currently ... > I made a tree providing a 2D array X (n1 samples , n2 features) and 1D array > Y (n1 corresponding classes ) such that Y(i) is the class of the sample X(i, > …) > After that I have correct predictions using predict() > Then I use the function > export_graphviz(clf, out_file=dot_data,feature_names=FEATURES) > with FEATURES being the array of my n2 features names in the same order as in > X > I obtain the tree .png but can’t find a way to have the correct class names > in the leaves… > In export_graphviz() should I use the class_names optional parameter and how ? > Thanks for any help > > Gregory, Toulouse FRANCE > > > > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn scikit-learn Info Page - Python<https://mail.python.org/mailman/listinfo/scikit-learn> mail.python.org To see the collection of prior postings to the list, visit the scikit-learn Archives. Using scikit-learn: To post a message to all the list members ... _______________________________________________ scikit-learn mailing list [email protected] https://mail.python.org/mailman/listinfo/scikit-learn scikit-learn Info Page - Python<https://mail.python.org/mailman/listinfo/scikit-learn> mail.python.org To see the collection of prior postings to the list, visit the scikit-learn Archives. Using scikit-learn: To post a message to all the list members ...
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