Difference between revisions of "Orange: Linear Regression"
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| − | + | Widget Linear Regression mengimplementasikan sebuah algoritma linear regression dengan regularisasi optional L1 (LASSO), L2 (ridge) atau L1L2 (elastic net). | |
| − | + | ==Input== | |
| − | + | Data: input dataset | |
| + | Preprocessor: preprocessing method(s) | ||
| − | + | ==Output== | |
| − | + | Learner: linear regression learning algorithm | |
| + | Model: trained model | ||
| + | Coefficients: linear regression coefficients | ||
| − | + | Widget Linear Regression membangun learner/predictor yang akan learn fungsi linear function dari input data-nya. Model dapat mengidentifikasi hubungan antara predictor xi dan response variable y. Di samping itu, parameter regularisasi Lasso and Ridge dapat di spesifikasikan. Lasso regression meminimalisasi penalized version dari least squares loss function dengan L1-norm penalty sedangkan regularisai Ridge dengan L2-norm penalty. | |
| − | + | Widget Linear Regression hanya dapat berfungsi / bekerja pada regression task. | |
| − | + | [[File:LinearRegression-stamped.png|center|200px|thumb]] | |
| − | The | + | * The learner/predictor name |
| + | * Choose a model to train: | ||
| + | ** no regularization | ||
| + | ** a Ridge regularization (L2-norm penalty) | ||
| + | ** a Lasso bound (L1-norm penalty) | ||
| + | ** an Elastic net regularization | ||
| − | + | * Produce a report. | |
| + | * Press Apply to commit changes. If Apply Automatically is ticked, changes are committed automatically. | ||
| − | + | ==Contoh== | |
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| + | Di bawah ini adalah workflow sederhana dengan housing dataset. Kita men-train widget Linear Regression dan widget Random Forest dan mengevaluasi performance-nya di widget Test & Score. | ||
| + | [[File:LinearRegression-regression.png|center|600px|thumb]] | ||
==Referensi== | ==Referensi== | ||
Latest revision as of 09:11, 6 April 2020
Sumber: https://docs.biolab.si//3/visual-programming/widgets/model/linearregression.html
Widget Linear Regression mengimplementasikan sebuah algoritma linear regression dengan regularisasi optional L1 (LASSO), L2 (ridge) atau L1L2 (elastic net).
Input
Data: input dataset Preprocessor: preprocessing method(s)
Output
Learner: linear regression learning algorithm Model: trained model Coefficients: linear regression coefficients
Widget Linear Regression membangun learner/predictor yang akan learn fungsi linear function dari input data-nya. Model dapat mengidentifikasi hubungan antara predictor xi dan response variable y. Di samping itu, parameter regularisasi Lasso and Ridge dapat di spesifikasikan. Lasso regression meminimalisasi penalized version dari least squares loss function dengan L1-norm penalty sedangkan regularisai Ridge dengan L2-norm penalty.
Widget Linear Regression hanya dapat berfungsi / bekerja pada regression task.
- The learner/predictor name
- Choose a model to train:
- no regularization
- a Ridge regularization (L2-norm penalty)
- a Lasso bound (L1-norm penalty)
- an Elastic net regularization
- Produce a report.
- Press Apply to commit changes. If Apply Automatically is ticked, changes are committed automatically.
Contoh
Di bawah ini adalah workflow sederhana dengan housing dataset. Kita men-train widget Linear Regression dan widget Random Forest dan mengevaluasi performance-nya di widget Test & Score.
