Difference between revisions of "Orange: AdaBoost"

Revision as of 04:24, 7 March 2020

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

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 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. We menggunakan AdaBoost, Tree dan Logistic Regression dan mengevaluasi performance model di widget Test & Score.

Untuk regression, kita load housing dataset, kirim data instances ke dua model yang berbeda (AdaBoost dan Tree) dan keluarannya ke widget Predictions.

Referensi

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

Pranala Menarik

Orange

@@ Line 3: / Line 3: @@
-An ensemble meta-algorithm that combines weak learners and adapts to the ‘hardness’ of each training sample.
+Sebuah ensemble meta-algoritma yang menggabungkan learner yang lemah dan beradaptasi dengan 'kekerasan' masing-masing sampel training.
 ==Input==
@@ Line 16: / Line 16: @@
   Model: trained model
-The AdaBoost (short for “Adaptive boosting”) widget is a machine-learning algorithm, formulated by Yoav Freund and Robert Schapire. It can be used with other learning algorithms to boost their performance. It does so by tweaking the weak learners.
+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 works for both classification and regression.
+AdaBoost dapat bekerja baik untuk task classification maupun regression.
 [[File:AdaBoost-stamped.png|center|200px|thumb]]
@@ Line 37: / Line 37: @@
 ==Contoh==
-For classification, we loaded the iris dataset. We used AdaBoost, Tree and Logistic Regression and evaluated the models’ performance in Test & Score.
+Untuk classification, kita load iris dataset. We menggunakan AdaBoost, Tree dan Logistic Regression dan mengevaluasi performance model di widget Test & Score.
 [[File:AdaBoost-classification.png|center|200px|thumb]]
-For regression, we loaded the housing dataset, sent the data instances to two different models (AdaBoost and Tree) and output them to the Predictions widget.
+Untuk regression, kita load housing dataset, kirim data instances ke dua model yang berbeda (AdaBoost dan Tree) dan keluarannya ke widget Predictions.
 [[File:AdaBoost-regression.png|center|200px|thumb]]