tree visualization with class names in leaves
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
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 <greg315@hotmail.fr> wrote:
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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
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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 <se.raschka@gmail.com> 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 <greg315@hotmail.fr> wrote:
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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
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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 <<mailto:se.raschka@gmail.com>se.raschka@gmail.com<mailto:se.raschka@gmail.com>> 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 <<mailto:greg315@hotmail.fr>greg315@hotmail.fr<mailto:greg315@hotmail.fr>> wrote:
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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 scikit-learn@python.org<mailto:scikit-learn@python.org> https://mail.python.org/mailman/listinfo/scikit-learn
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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 <piotr.bialecki@hotmail.de> 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 < <se.raschka@gmail.com>se.raschka@gmail.com> 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 < <greg315@hotmail.fr> greg315@hotmail.fr> wrote:
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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 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
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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.fr@python.org> de la part de Sebastian Raschka <se.raschka@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 <greg315@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… [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
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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/3a106fc792eb8e70e1fd078e35...)
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 <greg315@hotmail.fr> wrote:
clf.classes_ where clf is my tree. And with export_graphviz(clf, out_file=dot_data,feature_names=FEATURES,class_names=clf.classes_)
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 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.fr@python.org> de la part de Sebastian Raschka <se.raschka@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 <greg315@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 ...
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participants (5)
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greg g -
Piotr Bialecki -
Sebastian Raschka -
Sebastian Raschka -
Startup Hire