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Onnowpurbo: Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/visualize/treeviewer.html A visualization of classification and regression trees. Inputs Tree: decision t..."

2020-01-12T12:25:26Z

Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/visualize/treeviewer.html A visualization of classification and regression trees. Inputs Tree: decision t..."

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Sumber: https://docs.biolab.si//3/visual-programming/widgets/visualize/treeviewer.html

A visualization of classification and regression trees.

Inputs

Tree: decision tree

Outputs

Selected Data: instances selected from the tree node

Data: data with an additional column showing whether a point is selected

This is a versatile widget with 2-D visualization of classification and regression trees. The user can select a node, instructing the widget to output the data associated with the node, thus enabling explorative data analysis.

../../_images/TreeViewer-stamped.png

Information on the input.

Display options:

Zoom in and zoom out

Select the tree width. The nodes display information bubbles when hovering over them.

Select the depth of your tree.

Select edge width. The edges between the nodes in the tree graph are drawn based on the selected edge width.

All the edges will be of equal width if Fixed is chosen.

When Relative to root is selected, the width of the edge will correspond to the proportion of instances in the corresponding node with respect to all the instances in the training data. Under this selection, the edge will get thinner and thinner when traversing toward the bottom of the tree.

Relative to parent makes the edge width correspond to the proportion of instances in the nodes with respect to the instances in their parent node.

Define the target class, which you can change based on classes in the data.

Press Save image to save the created tree graph to your computer as a .svg or .png file.

Produce a report.

Examples

Below, is a simple classification schema, where we have read the data, constructed the decision tree and viewed it in our Tree Viewer. If both the viewer and Tree are open, any re-run of the tree induction algorithm will immediately affect the visualization. You can thus use this combination to explore how the parameters of the induction algorithm influence the structure of the resulting tree.

../../_images/TreeViewer-classification.png

Clicking on any node will output the related data instances. This is explored in the schema below that shows the subset in the data table and in the Scatter Plot. Make sure that the tree data is passed as a data subset; this can be done by connecting the Scatter Plot to the File widget first, and connecting it to the Tree Viewer widget next. Selected data will be displayed as bold dots.

Tree Viewer can also export labeled data. Connect Data Table to Tree Viewer and set the link between widgets to Data instead of Selected Data. This will send the entire data to Data Table with an additional meta column labeling selected data instances (Yes for selected and No for the remaining).

../../_images/TreeViewer-selection.png

Finally, Tree Viewer can be used also for visualizing regression trees. Connect Random Forest to File widget using housing.tab dataset. Then connect Pythagorean Forest to Random Forest. In Pythagorean Forest select a regression tree you wish to further analyze and pass it to the Tree Viewer. The widget will display the constructed tree. For visualizing larger trees, especially for regression, Pythagorean Tree could be a better option.

../../_images/TreeViewer-regression.png

==Referensi==

* https://docs.biolab.si//3/visual-programming/widgets/visualize/treeviewer.html

==Pranala Menarik==

* [[Orange]]

@@ Line 44: / Line 44: @@
 Widget Tree Viewer juga dapat meng-export labeled data. Connect Data Table ke widget Tree Viewer dan set link antara widget ke Data bukan Selected Data. Ini akan mengirim seluruh data ke Data Table dengan tambahan kolom meta labeling selected data instances (Yes untuk selected dan No untuk remaining).
-[[File:TreeViewer-selection.png|center|200px|thumb]]
+[[File:TreeViewer-selection.png|center|600px|thumb]]
 Akhirnya, widget Tree Viewer dapat digunakan untuk mem-visualisasi regression tree. Connect widget Random Forest ke widget File untuk mengambil dataset housing.tab. Kemudian sambungkan widget Pythagorean Forest ke widget Random Forest. Di widget Pythagorean Forest pilih regression tree yang ingin kita analisa lebih lanjut dan sambungkan ke widget Tree Viewer. Widget Tree Viewer akan menayangkan tree yang dibuat. Untuk mem-visualisasi tree yang besar, widget Pythagorean Tree akan menjadi pilihan yang lebih baik.
-[[File:TreeViewer-regression.png|center|200px|thumb]]
+[[File:TreeViewer-regression.png|center|600px|thumb]]
 ==Referensi==

@@ Line 38: / Line 38: @@
 Di bawah ini adalah workflow klasifikasi sederhana, data di load menggunakan widget File. Decision Tree dibuat menggunakan widget Tree dan kemudian di lihat menggunakan widget Tree Viewer. Jika ke dua widget Tree Viewer dan widget Tree dibuka bersamaan, re-run tree induction algorithm akan secara langsung berdampak pada visualisasi. Oleh karenanya, kita dapat menggunakan workflow ini untuk meng-explorasi bagaimana parameter dari tree induction algorithm mempengaruhi tree yang di hasilkan.
-[[File:TreeViewer-classification.png|center|200px|thumb]]
+[[File:TreeViewer-classification.png|center|600px|thumb]]
 Mengklik pada sembarang node akan menampilkan instance data terkait. Ini dieksplorasi dalam workflow di bawah ini yang menunjukkan subset data yang di sambungkan ke widget Scatter Plot. Pastikan bahwa data tree dilewatkan sebagai subset data; ini dapat dilakukan dengan menghubungkan widget Scatter Plot ke widget File terlebih dahulu, dan selanjutnya dihubungkan ke widget Tree Viewer. Data yang dipilih akan ditampilkan sebagai dot bold.
-Tree Viewer can also export labeled data. Connect Data Table to Tree Viewer and set the link between widgets to Data instead of Selected Data. This will send the entire data to Data Table with an additional meta column labeling selected data instances (Yes for selected and No for the remaining).
+Widget Tree Viewer juga dapat meng-export labeled data. Connect Data Table ke widget Tree Viewer dan set link antara widget ke Data bukan Selected Data. Ini akan mengirim seluruh data ke Data Table dengan tambahan kolom meta labeling selected data instances (Yes untuk selected dan No untuk remaining).
 [[File:TreeViewer-selection.png|center|200px|thumb]]
-Finally, Tree Viewer can be used also for visualizing regression trees. Connect Random Forest to File widget using housing.tab dataset. Then connect Pythagorean Forest to Random Forest. In Pythagorean Forest select a regression tree you wish to further analyze and pass it to the Tree Viewer. The widget will display the constructed tree. For visualizing larger trees, especially for regression, Pythagorean Tree could be a better option.
+Akhirnya, widget Tree Viewer dapat digunakan untuk mem-visualisasi regression tree. Connect widget Random Forest ke widget File untuk mengambil dataset housing.tab. Kemudian sambungkan widget Pythagorean Forest ke widget Random Forest. Di widget Pythagorean Forest pilih regression tree yang ingin kita analisa lebih lanjut dan sambungkan ke widget Tree Viewer. Widget Tree Viewer akan menayangkan tree yang dibuat. Untuk mem-visualisasi tree yang besar, widget Pythagorean Tree akan menjadi pilihan yang lebih baik.
 [[File:TreeViewer-regression.png|center|200px|thumb]]

@@ Line 40: / Line 40: @@
 [[File:TreeViewer-classification.png|center|200px|thumb]]
-Mengklik pada sembarang node akan menampilkan instance data terkait. Ini dieksplorasi dalam workflow di bawah ini yang menunjukkan subset di widget Data Table dan di widget Scatter Plot. Pastikan bahwa data tree dilewatkan sebagai subset data; ini dapat dilakukan dengan menghubungkan widget Scatter Plot ke widget File terlebih dahulu, dan selanjutnya dihubungkan ke widget Tree Viewer. Data yang dipilih akan ditampilkan sebagai dot bold.
+Mengklik pada sembarang node akan menampilkan instance data terkait. Ini dieksplorasi dalam workflow di bawah ini yang menunjukkan subset data yang di sambungkan ke widget Scatter Plot. Pastikan bahwa data tree dilewatkan sebagai subset data; ini dapat dilakukan dengan menghubungkan widget Scatter Plot ke widget File terlebih dahulu, dan selanjutnya dihubungkan ke widget Tree Viewer. Data yang dipilih akan ditampilkan sebagai dot bold.
 Tree Viewer can also export labeled data. Connect Data Table to Tree Viewer and set the link between widgets to Data instead of Selected Data. This will send the entire data to Data Table with an additional meta column labeling selected data instances (Yes for selected and No for the remaining).

@@ Line 36: / Line 36: @@
 ==Contoh==
-Di bawah ini adalah workflow klasifikasi sederhana, data di load menggunakan widget File. Decision Tree dibuat menggunakan widget Tree dan kemudian di lihat menggunakan widget Tree Viewer. Jika ke dua widget Tree Viewer dan widget Tree dibuka bersamaan,
+Di bawah ini adalah workflow klasifikasi sederhana, data di load menggunakan widget File. Decision Tree dibuat menggunakan widget Tree dan kemudian di lihat menggunakan widget Tree Viewer. Jika ke dua widget Tree Viewer dan widget Tree dibuka bersamaan, re-run tree induction algorithm akan secara langsung berdampak pada visualisasi. Oleh karenanya, kita dapat menggunakan workflow ini untuk meng-explorasi bagaimana parameter dari tree induction algorithm mempengaruhi tree yang di hasilkan.
 [[File:TreeViewer-classification.png|center|200px|thumb]]
-Clicking on any node will output the related data instances. This is explored in the schema below that shows the subset in the data table and in the Scatter Plot. Make sure that the tree data is passed as a data subset; this can be done by connecting the Scatter Plot to the File widget first, and connecting it to the Tree Viewer widget next. Selected data will be displayed as bold dots.
+Mengklik pada sembarang node akan menampilkan instance data terkait. Ini dieksplorasi dalam workflow di bawah ini yang menunjukkan subset di widget Data Table dan di widget Scatter Plot. Pastikan bahwa data tree dilewatkan sebagai subset data; ini dapat dilakukan dengan menghubungkan widget Scatter Plot ke widget File terlebih dahulu, dan selanjutnya dihubungkan ke widget Tree Viewer. Data yang dipilih akan ditampilkan sebagai dot bold.
 Tree Viewer can also export labeled data. Connect Data Table to Tree Viewer and set the link between widgets to Data instead of Selected Data. This will send the entire data to Data Table with an additional meta column labeling selected data instances (Yes for selected and No for the remaining).

@@ Line 36: / Line 36: @@
 ==Contoh==
-Below, is a simple classification schema, where we have read the data, constructed the decision tree and viewed it in our Tree Viewer. If both the viewer and Tree are open, any re-run of the tree induction algorithm will immediately affect the visualization. You can thus use this combination to explore how the parameters of the induction algorithm influence the structure of the resulting tree.
+Di bawah ini adalah workflow klasifikasi sederhana, data di load menggunakan widget File. Decision Tree dibuat menggunakan widget Tree dan kemudian di lihat menggunakan widget Tree Viewer. Jika ke dua widget Tree Viewer dan widget Tree dibuka bersamaan,
 [[File:TreeViewer-classification.png|center|200px|thumb]]
@@ Line 49: / Line 51: @@
 [[File:TreeViewer-regression.png|center|200px|thumb]]
 ==Referensi==

@@ Line 2: / Line 2: @@
+Widget Tree Viewer mem-visualiasi classification tree dan regression tree.
 ==Input==
@@ Line 14: / Line 13: @@
   Data: data with an additional column showing whether a point is selected
-This is a versatile widget with 2-D visualization of classification and regression trees. The user can select a node, instructing the widget to output the data associated with the node, thus enabling explorative data analysis.
+Widget Tree Viewer adalah widget serbaguna dengan visualisasi 2-D classification tree and regression tree. Pengguna dapat memilih node, menginstruksikan widget untuk menampilkan data yang terkait dengan node, sehingga memungkinkan analisis data eksploratif.
-[[File:TreeViewer-stamped.png|center|200px|thumb]]
+[[File:TreeViewer-stamped.png|center|600px|thumb]]
 * Information on the input.

@@ Line 5: / Line 5: @@
 A visualization of classification and regression trees.
-Inputs
+==Input==
-    Tree: decision tree
+ Tree: decision tree
-Outputs
+==Output==
-    Selected Data: instances selected from the tree node
+ Selected Data: instances selected from the tree node
-    Data: data with an additional column showing whether a point is selected
+ Data: data with an additional column showing whether a point is selected
 This is a versatile widget with 2-D visualization of classification and regression trees. The user can select a node, instructing the widget to output the data associated with the node, thus enabling explorative data analysis.
@@ Line 18: / Line 18: @@
 [[File:TreeViewer-stamped.png|center|200px|thumb]]
-    Information on the input.
+* Information on the input.
-    Display options:
+** Zoom in and zoom out
-        Zoom in and zoom out
+** Select the tree width. The nodes display information bubbles when hovering over them.
-        Select the tree width. The nodes display information bubbles when hovering over them.
+** Select the depth of your tree.
-        Select the depth of your tree.
+** Select edge width. The edges between the nodes in the tree graph are drawn based on the selected edge width.
-        Select edge width. The edges between the nodes in the tree graph are drawn based on the selected edge width.
-            All the edges will be of equal width if Fixed is chosen.
+*** All the edges will be of equal width if Fixed is chosen.
-            When Relative to root is selected, the width of the edge will correspond to the proportion of instances in the corresponding node with respect to all the instances in the training data. Under this selection, the edge will get thinner and thinner when traversing toward the bottom of the tree.
+*** When Relative to root is selected, the width of the edge will correspond to the proportion of instances in the corresponding node with respect to all the instances in the training data. Under this selection, the edge will get thinner and thinner when traversing toward the bottom of the tree.
-            Relative to parent makes the edge width correspond to the proportion of instances in the nodes with respect to the instances in their parent node.
+*** Relative to parent makes the edge width correspond to the proportion of instances in the nodes with respect to the instances in their parent node.
-        Define the target class, which you can change based on classes in the data.
-    Press Save image to save the created tree graph to your computer as a .svg or .png file.
+**        Define the target class, which you can change based on classes in the data.
-    Produce a report.
 ==Contoh==