Hi Piotr, Thanks for the reply.
I understand the thresholds at the current node. I was referring to this: Consider the node: Duration <= 0.5 having gini = 0.3386 and samples = 327510 What is meant by this: value = [216974.9673, 59743.3314] Regards, Sanant On Tue, Oct 25, 2016 at 3:02 PM, Piotr Bialecki <[email protected]> wrote: > Hi Sanant, > > the values represent the thresholds at the current feature (node), which > are used to classify the next sample. > > You can see an example here: > http://scikit-learn.org/stable/modules/tree.html > > The first node uses the feature "petal length (cm)" with a threshold of > 2.45. > > If your future sample as a petal length <= 2.45cm it will be pushed into > the left branch and therefore will be classifies as class = setosa. > However, if the petal length is > 2.45cm, it will be pushed into the right > branch and the next node (feature) is evalueted. > > I hope I understood your question correct. > > > Best regards, > Piotr > > > > > On 25.10.2016 08:41, Startup Hire wrote: > > Hi all, > > Thanks for the suggestion. > > I have a related question on tree visualization > > I have 2 classes to predict: 0 and 1 (it comes up as a numeric field when > I load the dataset) > > I have given the class_names as "NotPresent" and "Ispresent" which I > believe it will map to 0 and 1. is that correct? > > > How do I interpret the nodes and value present in each nodes in the > accompanying diagram? > > Regards, > Sanant > > > > > On Mon, Oct 24, 2016 at 9:17 PM, Sebastian Raschka < > <[email protected]>[email protected]> wrote: > >> 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]> >> [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-08d3fc >> 1895c4 >> > 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… >> > 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 mailing list >> [email protected] >> https://mail.python.org/mailman/listinfo/scikit-learn >> > > > > _______________________________________________ > scikit-learn mailing > [email protected]https://mail.python.org/mailman/listinfo/scikit-learn > > > > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn > >
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