Difference between revisions of "Orange: AdaBoost"
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| − | + | Widget AdaBoost adalah sebuah ensemble meta-algoritma yang menggabungkan learner yang lemah dan beradaptasi dengan 'kekerasan' masing-masing sampel training. | |
| − | + | ==Input== | |
| − | + | Data: input dataset | |
| − | + | Preprocessor: preprocessing method(s) | |
| − | + | Learner: learning algorithm | |
| − | + | ==Output== | |
| − | + | Learner: AdaBoost learning algorithm | |
| − | + | Model: trained model | |
| − | + | Widget AdaBoost (singkatan dari “Adaptive boosting”) adalah algoritma machine-learning, yang di formulasikan oleh Yoav Freund and Robert Schapire. Ini dapat digunakan dengan algoritma learner lainnya untuk meningkatkan kinerja mereka. Itu dilakukan dengan mengutak-atik learner yang lemah. | |
| − | AdaBoost | + | Widget AdaBoost dapat bekerja baik untuk task classification maupun regression. |
[[File:AdaBoost-stamped.png|center|200px|thumb]] | [[File:AdaBoost-stamped.png|center|200px|thumb]] | ||
| − | + | * The learner can be given a name under which it will appear in other widgets. The default name is “AdaBoost”. | |
| − | + | * Set the parameters. The base estimator is a tree and you can set: | |
| + | ** Number of estimators | ||
| + | ** Learning rate: it determines to what extent the newly acquired information will override the old information (0 = the agent will not learn anything, 1 = the agent considers only the most recent information) | ||
| + | ** Fixed seed for random generator: set a fixed seed to enable reproducing the results. | ||
| − | + | * Boosting method. | |
| − | + | ** Classification algorithm (if classification on input): SAMME (updates base estimator’s weights with classification results) or SAMME.R (updates base estimator’s weight with probability estimates). | |
| − | + | ** Regression loss function (if regression on input): Linear (), Square (), Exponential (). | |
| − | + | * Produce a report. | |
| − | + | * Click Apply after changing the settings. That will put the new learner in the output and, if the training examples are given, construct a new model and output it as well. To communicate changes automatically tick Apply Automatically. | |
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==Contoh== | ==Contoh== | ||
| − | + | Untuk classification, kita load iris dataset. Kita menggunakan widget AdaBoost, widget Tree dan widget Logistic Regression dan mengevaluasi performance model di widget Test & Score. | |
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| + | [[File:AdaBoost-classification.png|center|600px|thumb]] | ||
| + | Untuk regression, kita load housing dataset, kirim data instances ke dua model yang berbeda (widget AdaBoost dan widget Tree) dan keluarannya ke widget Predictions. | ||
| + | [[File:AdaBoost-regression.png|center|600px|thumb]] | ||
==Referensi== | ==Referensi== | ||
Latest revision as of 10:14, 6 April 2020
Sumber: https://docs.biolab.si//3/visual-programming/widgets/model/adaboost.html
Widget AdaBoost adalah sebuah ensemble meta-algoritma yang menggabungkan learner yang lemah dan beradaptasi dengan 'kekerasan' masing-masing sampel training.
Input
Data: input dataset Preprocessor: preprocessing method(s) Learner: learning algorithm
Output
Learner: AdaBoost learning algorithm Model: trained model
Widget AdaBoost (singkatan dari “Adaptive boosting”) adalah algoritma machine-learning, yang di formulasikan oleh Yoav Freund and Robert Schapire. Ini dapat digunakan dengan algoritma learner lainnya untuk meningkatkan kinerja mereka. Itu dilakukan dengan mengutak-atik learner yang lemah.
Widget AdaBoost dapat bekerja baik untuk task classification maupun regression.
- The learner can be given a name under which it will appear in other widgets. The default name is “AdaBoost”.
- Set the parameters. The base estimator is a tree and you can set:
- Number of estimators
- Learning rate: it determines to what extent the newly acquired information will override the old information (0 = the agent will not learn anything, 1 = the agent considers only the most recent information)
- Fixed seed for random generator: set a fixed seed to enable reproducing the results.
- Boosting method.
- Classification algorithm (if classification on input): SAMME (updates base estimator’s weights with classification results) or SAMME.R (updates base estimator’s weight with probability estimates).
- Regression loss function (if regression on input): Linear (), Square (), Exponential ().
- Produce a report.
- Click Apply after changing the settings. That will put the new learner in the output and, if the training examples are given, construct a new model and output it as well. To communicate changes automatically tick Apply Automatically.
Contoh
Untuk classification, kita load iris dataset. Kita menggunakan widget AdaBoost, widget Tree dan widget Logistic Regression dan mengevaluasi performance model di widget Test & Score.
Untuk regression, kita load housing dataset, kirim data instances ke dua model yang berbeda (widget AdaBoost dan widget Tree) dan keluarannya ke widget Predictions.
