Hi, Gregory, > I dont't use the iris dataset. My classes are distributed in my Y array.
Yeah, I just used this as a simple example :). > the nodes of the graphical tree seem to be filled with the predominant class I think that’s right, it gets the class name of the majority class at each node via "class_name = class_names[np.argmax(value)]” (https://github.com/scikit-learn/scikit-learn/blob/3a106fc792eb8e70e1fd078e351ba42487d3214d/sklearn/tree/export.py#L286) > in a vector with the classes in alphabetical order ( the same order as in > clf.classes_) yes, it should be in ascending, alpha numerical order. Not sure if this is still a general recommendation in the sklearn 0.18, but I typically convert string class labels to integers before I feed it to a classifier (but it seems to work either way now) -> from sklearn.preprocessing import LabelEncoder -> le = LabelEncoder() -> y = le.fit_transform(labels) -> le.classes_ array(['Setosa', 'Versicolor', 'Virginica'], dtype='<U21’) -> import numpy as np -> np.bincount(y) array([50, 50, 50]) Best, Sebastian > On Oct 25, 2016, at 3:00 AM, greg g <greg...@hotmail.fr> wrote: > > 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 <scikit-learn-bounces+greg315=hotmail...@python.org> de la > part de Sebastian Raschka <se.rasc...@gmail.com> > 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 <greg...@hotmail.fr> 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… > > 1.10. Decision Trees — scikit-learn 0.18 documentation > 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 > > scikit-learn@python.org > > https://mail.python.org/mailman/listinfo/scikit-learn > scikit-learn Info Page - Python > 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 > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > scikit-learn Info Page - Python > 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 > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
