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Onnowpurbo: Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/model/tree.html A tree algorithm with forward pruning. Inputs Data: input dataset Preprocessor: prep..."

2020-01-12T13:35:15Z

Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/model/tree.html A tree algorithm with forward pruning. Inputs Data: input dataset Preprocessor: prep..."

New page

Sumber: https://docs.biolab.si//3/visual-programming/widgets/model/tree.html

A tree algorithm with forward pruning.

Inputs

Data: input dataset

Preprocessor: preprocessing method(s)

Outputs

Learner: decision tree learning algorithm

Model: trained model

Tree is a simple algorithm that splits the data into nodes by class purity. It is a precursor to Random Forest. Tree in Orange is designed in-house and can handle both discrete and continuous datasets.

It can also be used for both classification and regression tasks.

../../_images/Tree-stamped.png

The learner can be given a name under which it will appear in other widgets. The default name is “Tree”.

Tree parameters:

Induce binary tree: build a binary tree (split into two child nodes)

Min. number of instances in leaves: if checked, the algorithm will never construct a split which would put less than the specified number of training examples into any of the branches.

Do not split subsets smaller than: forbids the algorithm to split the nodes with less than the given number of instances.

Limit the maximal tree depth: limits the depth of the classification tree to the specified number of node levels.

Stop when majority reaches [%]: stop splitting the nodes after a specified majority threshold is reached

Produce a report. After changing the settings, you need to click Apply, which will put the new learner on the output and, if the training examples are given, construct a new classifier and output it as well. Alternatively, tick the box on the left and changes will be communicated automatically.

Examples

There are two typical uses for this widget. First, you may want to induce a model and check what it looks like in Tree Viewer.

../../_images/Tree-classification-visualize.png

The second schema trains a model and evaluates its performance against Logistic Regression.

../../_images/Tree-classification-model.png

We used the iris dataset in both examples. However, Tree works for regression tasks as well. Use housing dataset and pass it to Tree. The selected tree node from Tree Viewer is presented in the Scatter Plot and we can see that the selected examples exhibit the same features.

../../_images/Tree-regression-subset.png

==Referensi==

* https://docs.biolab.si//3/visual-programming/widgets/model/tree.html

==Pranala Menarik==

* [[Orange]]

@@ Line 41: / Line 41: @@
 [[File:Tree-classification-model.png|center|200px|thumb]]
-Kita menggunakan iris dataset dalam ke dua contoh. Akan tetapi, Tree juga dapat bekerja untuk regression task juga. Gunakan housing dataset dan berikan itu ke Tree. Tree node yang di pilih dalam Tree Viewer di tampilkan di Scatter Plot dan kita bisa melihat bahwa contoh yang di pilih memiliki feature yang sama.
+Kita menggunakan iris dataset dalam ke dua contoh. Widget Tree dapat bekerja untuk task regression. Pada workflow di bawah ini menggunakan housing dataset dan berikan dataset ke widget Tree. Tree node yang di pilih dalam Tree Viewer akan di tampilkan di Scatter Plot dan kita bisa melihat bahwa contoh yang di pilih memiliki feature yang sama.
 [[File:Tree-regression-subset.png|center|200px|thumb]]

@@ Line 37: / Line 37: @@
 [[File:Tree-classification-visualize.png|center|200px|thumb]]
-Skema ke dua dengan men-train sebuah model dan mengevaluasinya terhadap Logistic Regression.
+Skema ke dua di workflow di bawah ini, widget Tree akan men-train sebuah model dan mengevaluasinya terhadap Logistic Regression. Perbandingan keakuratan widget Tree dan widget Logistic Regression di lakukan melalui widget Test & Score. Hasil widget Test & Score dapat dilihat melalui widget Confusion Matrix untuk membandingkan kesalahan klasifikasi yang terjadi.
 [[File:Tree-classification-model.png|center|200px|thumb]]

@@ Line 17: / Line 17: @@
 Algoritma Tree adalah algoritma sederhana yang dapat memisahkan data menjadi node berdasarkan class purity (kemurnian kategori / class). Algoritma Tree adalah pendahulu Algoritma Random Forest. Widget Tree dalam Orange dirancang secara in-house dan dapat menangani dataset diskrit dan kontinyu.
-Tree juga menggunakan kedua classification dan regression.
+Widget Tree dapat digunakan untuk task classification dan task regression.
 [[File:Tree-stamped.png|center|200px|thumb]]

@@ Line 3: / Line 3: @@
-Algoritma tree dengan kemampuan untuk melakukan forward pruning (pemangkasan ke depan).
+Widget Tree menggunakan algoritma Tree dengan kemampuan untuk melakukan forward pruning (pemangkasan ke depan).
 ==Input==
@@ Line 15: / Line 15: @@
   Model: trained model
-Tree adalah algoritma sederhana yang dapat memisahkan data menjadi node berdasarkan class purity (kemurnian kategori / class). Ini adalah pendahulu Algoritma Random Forest. Tree dalam Orange dirancang secara in-house dan dapat menangani dataset diskrit dan kontinyu.
+Algoritma Tree adalah algoritma sederhana yang dapat memisahkan data menjadi node berdasarkan class purity (kemurnian kategori / class). Algoritma Tree adalah pendahulu Algoritma Random Forest. Widget Tree dalam Orange dirancang secara in-house dan dapat menangani dataset diskrit dan kontinyu.
 Tree juga menggunakan kedua classification dan regression.
@@ Line 33: / Line 33: @@
 ==Contoh==
-Ada dua penggunaan yang biasanya digunakan pada widget tree. Pertama, kita dapat meng-induksi sebuah model dan cek menggunakan tampilan yang seperti Tree Viewer.
+Ada dua penggunaan yang biasanya digunakan pada widget Tree. Pertama, kita dapat meng-induksi sebuah model dan cek menggunakan tampilan yang seperti widget Tree Viewer.
 [[File:Tree-classification-visualize.png|center|200px|thumb]]

@@ Line 41: / Line 41: @@
 [[File:Tree-classification-model.png|center|200px|thumb]]
-Kita menggunakan iris dataset dalam ke dua contoh. Akan tetapi, Tree juga dapat bekerja untuk regression task juga. Gunakan housing dataset dan berikan itu ke Tree. The selected tree node from Tree Viewer is presented in the Scatter Plot and we can see that the selected examples exhibit the same features.
+Kita menggunakan iris dataset dalam ke dua contoh. Akan tetapi, Tree juga dapat bekerja untuk regression task juga. Gunakan housing dataset dan berikan itu ke Tree. Tree node yang di pilih dalam Tree Viewer di tampilkan di Scatter Plot dan kita bisa melihat bahwa contoh yang di pilih memiliki feature yang sama.
 [[File:Tree-regression-subset.png|center|200px|thumb]]
 ==Referensi==

@@ Line 5: / Line 5: @@
 A tree algorithm with forward pruning.
-Inputs
+==Input==
-    Data: input dataset
+ Data: input dataset
-    Preprocessor: preprocessing method(s)
+ Preprocessor: preprocessing method(s)
-Outputs
+==Output==
-    Learner: decision tree learning algorithm
+ Learner: decision tree learning algorithm
-    Model: trained model
+ Model: trained model
 Tree is a simple algorithm that splits the data into nodes by class purity. It is a precursor to Random Forest. Tree in Orange is designed in-house and can handle both discrete and continuous datasets.
@@ Line 21: / Line 21: @@
 [[File:Tree-stamped.png|center|200px|thumb]]
-    The learner can be given a name under which it will appear in other widgets. The default name is “Tree”.
+* The learner can be given a name under which it will appear in other widgets. The default name is “Tree”.
-    Tree parameters:
+* Stop when majority reaches [%]: stop splitting the nodes after a specified majority threshold is reached
-        Induce binary tree: build a binary tree (split into two child nodes)
+* Produce a report. After changing the settings, you need to click Apply, which will put the new learner on the output and, if the training examples are given, construct a new classifier and output it as well. * Alternatively, tick the box on the left and changes will be communicated automatically.
 ==Contoh==

@@ Line 8: / Line 8: @@
      Data: input dataset
      Preprocessor: preprocessing method(s)
@@ Line 14: / Line 13: @@
      Learner: decision tree learning algorithm
      Model: trained model
@@ Line 21: / Line 19: @@
 It can also be used for both classification and regression tasks.
-../../_images/Tree-stamped.png
+[[File:Tree-stamped.png|center|200px|thumb]]
      The learner can be given a name under which it will appear in other widgets. The default name is “Tree”.
      Tree parameters:
          Induce binary tree: build a binary tree (split into two child nodes)
          Min. number of instances in leaves: if checked, the algorithm will never construct a split which would put less than the specified number of training examples into any of the branches.
          Do not split subsets smaller than: forbids the algorithm to split the nodes with less than the given number of instances.
          Limit the maximal tree depth: limits the depth of the classification tree to the specified number of node levels.
      Stop when majority reaches [%]: stop splitting the nodes after a specified majority threshold is reached
      Produce a report. After changing the settings, you need to click Apply, which will put the new learner on the output and, if the training examples are given, construct a new classifier and output it as well. Alternatively, tick the box on the left and changes will be communicated automatically.
-Examples
+==Contoh==
 There are two typical uses for this widget. First, you may want to induce a model and check what it looks like in Tree Viewer.
-../../_images/Tree-classification-visualize.png
+[[File:Tree-classification-visualize.png|center|200px|thumb]]
 The second schema trains a model and evaluates its performance against Logistic Regression.
-../../_images/Tree-classification-model.png
+[[File:Tree-classification-model.png|center|200px|thumb]]
 We used the iris dataset in both examples. However, Tree works for regression tasks as well. Use housing dataset and pass it to Tree. The selected tree node from Tree Viewer is presented in the Scatter Plot and we can see that the selected examples exhibit the same features.
-../../_images/Tree-regression-subset.png
+[[File:Tree-regression-subset.png|center|200px|thumb]]