Difference between revisions of "Orange: Linear Regression"
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Data: input dataset | Data: input dataset | ||
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Preprocessor: preprocessing method(s) | Preprocessor: preprocessing method(s) | ||
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Learner: linear regression learning algorithm | Learner: linear regression learning algorithm | ||
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Model: trained model | Model: trained model | ||
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Coefficients: linear regression coefficients | Coefficients: linear regression coefficients | ||
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Linear regression works only on regression tasks. | Linear regression works only on regression tasks. | ||
| − | + | [[File:LinearRegression-stamped.png|center|200px|thumb]] | |
The learner/predictor name | The learner/predictor name | ||
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Choose a model to train: | Choose a model to train: | ||
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no regularization | no regularization | ||
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a Ridge regularization (L2-norm penalty) | a Ridge regularization (L2-norm penalty) | ||
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a Lasso bound (L1-norm penalty) | a Lasso bound (L1-norm penalty) | ||
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an Elastic net regularization | an Elastic net regularization | ||
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Produce a report. | Produce a report. | ||
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Press Apply to commit changes. If Apply Automatically is ticked, changes are committed automatically. | 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. | Below, is a simple workflow with housing dataset. We trained Linear Regression and Random Forest and evaluated their performance in Test & Score. | ||
| − | + | [[File:LinearRegression-regression.png|center|200px|thumb]] | |
Revision as of 09:44, 23 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.
Inputs
Data: input dataset Preprocessor: preprocessing method(s)
Outputs
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.
