Difference between revisions of "Orange: Tree"

Revision as of 06:11, 3 March 2020

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

Algoritma tree dengan kemampuan untuk melakukan forward pruning (pemangkasan ke depan).

Input

Data: input dataset
Preprocessor: preprocessing method(s)

Output

Learner: decision tree learning algorithm
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.

Tree juga menggunakan kedua classification dan regression.

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.

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.

Skema ke dua dengan men-train sebuah model dan mengevaluasinya terhadap Logistic Regression.

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.

Referensi

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

Pranala Menarik

Orange

@@ Line 3: / Line 3: @@
-A tree algorithm with forward pruning.
+Algoritma tree dengan kemampuan untuk melakukan forward pruning (pemangkasan ke depan).
 ==Input==
@@ Line 15: / Line 15: @@
   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.
+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.
-It can also be used for both classification and regression tasks.
+Tree juga menggunakan kedua classification dan regression.
 [[File:Tree-stamped.png|center|200px|thumb]]
@@ Line 33: / Line 33: @@
 ==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.
+Ada dua penggunaan yang biasanya digunakan pada widget tree. Pertama, kita dapat meng-induksi sebuah model dan cek menggunakan tampilan yang seperti Tree Viewer.
 [[File:Tree-classification-visualize.png|center|200px|thumb]]
-The second schema trains a model and evaluates its performance against Logistic Regression.
+Skema ke dua dengan men-train sebuah model dan mengevaluasinya terhadap Logistic Regression.
 [[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.
+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.
 [[File:Tree-regression-subset.png|center|200px|thumb]]