Difference between revisions of "Orange: Random Forest"

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Predict using an ensemble of decision trees.
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Widget Random Forest dapat memprediksi menggunakan sebuah ensemble (kumpulan / koleksi) dari decision tree.
  
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Inputs
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==Input==
  
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    Data: input dataset
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Data: input dataset
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    Preprocessor: preprocessing method(s)
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Preprocessor: preprocessing method(s)
  
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Outputs
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==Output==
  
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    Learner: random forest learning algorithm
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Learner: random forest learning algorithm
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    Model: trained model
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Model: trained model
  
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Random forest is an ensemble learning method used for classification, regression and other tasks. It was first proposed by Tin Kam Ho and further developed by Leo Breiman (Breiman, 2001) and Adele Cutler.
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Random forest adalah sebuah ensemble learning method yang digunakan untuk classification, regression dan tugas-tugas lainnya. Random forest pertama kali di usulkan oleh Tin Kam Ho dan di kembangkan lebih lanjut oleh Leo Breiman (Breiman, 2001) dan Adele Cutler.
  
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Random Forest builds a set of decision trees. Each tree is developed from a bootstrap sample from the training data. When developing individual trees, an arbitrary subset of attributes is drawn (hence the term “Random”), from which the best attribute for the split is selected. The final model is based on the majority vote from individually developed trees in the forest.
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Random Forest membangun sekumpulan decision tree. Setiap tree di kembangkan dari sebuah bootstrap sample dari training data. Ketika mengembangkan individual tree, subset acak dari atribut diambil (maka digunakan istilah "Random"), dari mana atribut terbaik untuk split (pemisahan) dipilih. Model terakhir didasarkan pada suara terbanyak dari individually developed tree di forest.
  
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Random Forest works for both classification and regression tasks.
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Random Forest dapat digunakan untuk task (tugas) klasifikasi dan regresi.
  
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[[File:RandomForest-stamped.png|center|200px|thumb]]
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[[File:RandomForest-stamped.png|center|400px|thumb]]
  
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    Specify the name of the model. The default name is “Random Forest”.
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* Specify the name of the model. The default name is “Random Forest”.
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    Specify how many decision trees will be included in the forest (Number of trees in the forest), and how many attributes will be arbitrarily drawn for consideration at each node. If the latter is not specified (option Number of attributes… left unchecked), this number is equal to the square root of the number of attributes in the data. You can also choose to fix the seed for tree generation (Fixed seed for random generator), which enables replicability of the results.
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* Specify how many decision trees will be included in the forest (Number of trees in the forest), and how many attributes will be arbitrarily drawn for consideration at each node. If the latter is not specified (option Number of attributes… left unchecked), this number is equal to the square root of the number of attributes in the data. You can also choose to fix the seed for tree generation (Fixed seed for random generator), which enables replicability of the results.
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    Original Breiman’s proposal is to grow the trees without any pre-pruning, but since pre-pruning often works quite well and is faster, the user can set the depth to which the trees will be grown (Limit depth of individual trees). Another pre-pruning option is to select the smallest subset that can be split (Do not split subsets smaller than).
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* Original Breiman’s proposal is to grow the trees without any pre-pruning, but since pre-pruning often works quite well and is faster, the user can set the depth to which the trees will be grown (Limit depth of individual trees). Another pre-pruning option is to select the smallest subset that can be split (Do not split subsets smaller than).
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    Produce a report.
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* Produce a report.
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    Click Apply to communicate the changes to other widgets. Alternatively, tick the box on the left side of the Apply button and changes will be communicated automatically.
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* Click Apply to communicate the changes to other widgets. Alternatively, tick the box on the left side of the Apply button and changes will be communicated automatically.
  
 
==Contoh==
 
==Contoh==
  
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For classification tasks, we use iris dataset. Connect it to Predictions. Then, connect File to Random Forest and Tree and connect them further to Predictions. Finally, observe the predictions for the two models.
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Untuk contoh classification task, kita menggunakan dataset iris. Menghubungkan Widget Random Forest ke Widget Predictions. Dengan menghubungkan Widget File ke Widget Random Forest dan Widget Tree dan hubungkan mereka ke Widget Predictions. Akhirnya, kita bisa memperhatikan prediksi dari ke dua model.
  
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[[File:RandomForest-classification.png|center|200px|thumb]]
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[[File:RandomForest-classification.png|center|600px|thumb]]
  
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For regressions tasks, we will use housing data. Here, we will compare different models, namely Random Forest, Linear Regression and Constant, in the Test & Score widget.
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Untuk contoh regression task, kita menggunakan dataset housing. Disini, kita akan membandingkan beberapa model yang berbeda, yaitu, widget Random Forest, widget Linear Regression dan widget Constant, di Widget Test & Score.
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[[File:RandomForest-regression.png|center|200px|thumb]]
 
  
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[[File:RandomForest-regression.png|center|600px|thumb]]
  
 
==Referensi==
 
==Referensi==

Latest revision as of 08:16, 6 April 2020

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


Widget Random Forest dapat memprediksi menggunakan sebuah ensemble (kumpulan / koleksi) dari decision tree.

Input

Data: input dataset
Preprocessor: preprocessing method(s)

Output

Learner: random forest learning algorithm
Model: trained model

Random forest adalah sebuah ensemble learning method yang digunakan untuk classification, regression dan tugas-tugas lainnya. Random forest pertama kali di usulkan oleh Tin Kam Ho dan di kembangkan lebih lanjut oleh Leo Breiman (Breiman, 2001) dan Adele Cutler.

Random Forest membangun sekumpulan decision tree. Setiap tree di kembangkan dari sebuah bootstrap sample dari training data. Ketika mengembangkan individual tree, subset acak dari atribut diambil (maka digunakan istilah "Random"), dari mana atribut terbaik untuk split (pemisahan) dipilih. Model terakhir didasarkan pada suara terbanyak dari individually developed tree di forest.

Random Forest dapat digunakan untuk task (tugas) klasifikasi dan regresi.

RandomForest-stamped.png
  • Specify the name of the model. The default name is “Random Forest”.
  • Specify how many decision trees will be included in the forest (Number of trees in the forest), and how many attributes will be arbitrarily drawn for consideration at each node. If the latter is not specified (option Number of attributes… left unchecked), this number is equal to the square root of the number of attributes in the data. You can also choose to fix the seed for tree generation (Fixed seed for random generator), which enables replicability of the results.
  • Original Breiman’s proposal is to grow the trees without any pre-pruning, but since pre-pruning often works quite well and is faster, the user can set the depth to which the trees will be grown (Limit depth of individual trees). Another pre-pruning option is to select the smallest subset that can be split (Do not split subsets smaller than).
  • Produce a report.
  • Click Apply to communicate the changes to other widgets. Alternatively, tick the box on the left side of the Apply button and changes will be communicated automatically.

Contoh

Untuk contoh classification task, kita menggunakan dataset iris. Menghubungkan Widget Random Forest ke Widget Predictions. Dengan menghubungkan Widget File ke Widget Random Forest dan Widget Tree dan hubungkan mereka ke Widget Predictions. Akhirnya, kita bisa memperhatikan prediksi dari ke dua model.

RandomForest-classification.png

Untuk contoh regression task, kita menggunakan dataset housing. Disini, kita akan membandingkan beberapa model yang berbeda, yaitu, widget Random Forest, widget Linear Regression dan widget Constant, di Widget Test & Score.

RandomForest-regression.png

Referensi

Breiman, L. (2001). Random Forests. In Machine Learning, 45(1), 5-32. Available here.


Referensi

Pranala Menarik

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