Difference between revisions of "Orange: Linear Regression"
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A linear regression algorithm with optional L1 (LASSO), L2 (ridge) or L1L2 (elastic net) regularization. | A linear regression algorithm with optional L1 (LASSO), L2 (ridge) or L1L2 (elastic net) regularization. | ||
− | + | ==Input== | |
− | + | Data: input dataset | |
− | + | Preprocessor: preprocessing method(s) | |
− | + | ==Output== | |
− | + | Learner: linear regression learning algorithm | |
− | + | Model: trained model | |
− | + | Coefficients: linear regression coefficients | |
The Linear Regression widget constructs a learner/predictor that learns a linear function from its input data. The model can identify the relationship between a predictor xi and the response variable y. Additionally, Lasso and Ridge regularization parameters can be specified. Lasso regression minimizes a penalized version of the least squares loss function with L1-norm penalty and Ridge regularization with L2-norm penalty. | The Linear Regression widget constructs a learner/predictor that learns a linear function from its input data. The model can identify the relationship between a predictor xi and the response variable y. Additionally, Lasso and Ridge regularization parameters can be specified. Lasso regression minimizes a penalized version of the least squares loss function with L1-norm penalty and Ridge regularization with L2-norm penalty. | ||
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[[File:LinearRegression-stamped.png|center|200px|thumb]] | [[File:LinearRegression-stamped.png|center|200px|thumb]] | ||
− | + | * 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== | ==Contoh== |
Revision as of 10:56, 28 January 2020
Sumber: https://docs.biolab.si//3/visual-programming/widgets/model/linearregression.html
A linear regression algorithm with optional L1 (LASSO), L2 (ridge) or L1L2 (elastic net) regularization.
Input
Data: input dataset Preprocessor: preprocessing method(s)
Output
Learner: linear regression learning algorithm Model: trained model Coefficients: linear regression coefficients
The Linear Regression widget constructs a learner/predictor that learns a linear function from its input data. The model can identify the relationship between a predictor xi and the response variable y. Additionally, Lasso and Ridge regularization parameters can be specified. Lasso regression minimizes a penalized version of the least squares loss function with L1-norm penalty and Ridge regularization with L2-norm penalty.
Linear regression works only on regression tasks.
- 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
Below, is a simple workflow with housing dataset. We trained Linear Regression and Random Forest and evaluated their performance in Test & Score.