Difference between revisions of "Orange: Logistic Regression"
Onnowpurbo (talk | contribs) |
Onnowpurbo (talk | contribs) |
||
| Line 4: | Line 4: | ||
The logistic regression classification algorithm with LASSO (L1) or ridge (L2) regularization. | The logistic regression classification algorithm with LASSO (L1) or ridge (L2) regularization. | ||
| − | + | ==Input== | |
| − | + | Data: input dataset | |
| − | + | Preprocessor: preprocessing method(s) | |
| − | + | ==Output== | |
| − | + | Learner: logistic regression learning algorithm | |
| − | + | Model: trained model | |
| − | + | Coefficients: logistic regression coefficients | |
Logistic Regression learns a Logistic Regression model from the data. It only works for classification tasks. | Logistic Regression learns a Logistic Regression model from the data. It only works for classification tasks. | ||
| Line 19: | Line 19: | ||
[[File:LogisticRegression-stamped.png|center|200px|thumb]] | [[File:LogisticRegression-stamped.png|center|200px|thumb]] | ||
| − | + | * A name under which the learner appears in other widgets. The default name is “Logistic Regression”. | |
| − | + | * Regularization type (either L1 or L2). Set the cost strength (default is C=1). | |
| − | + | * Press Apply to commit changes. If Apply Automatically is ticked, changes will be communicated automatically. | |
==Contoh== | ==Contoh== | ||
| Line 30: | Line 30: | ||
[[File:LogisticRegression-classification.png|center|200px|thumb]] | [[File:LogisticRegression-classification.png|center|200px|thumb]] | ||
| − | |||
Contoh Workflow lain, | Contoh Workflow lain, | ||
Revision as of 10:57, 28 January 2020
Sumber: https://docs.biolab.si//3/visual-programming/widgets/model/logisticregression.html
The logistic regression classification algorithm with LASSO (L1) or ridge (L2) regularization.
Input
Data: input dataset Preprocessor: preprocessing method(s)
Output
Learner: logistic regression learning algorithm Model: trained model Coefficients: logistic regression coefficients
Logistic Regression learns a Logistic Regression model from the data. It only works for classification tasks.
- A name under which the learner appears in other widgets. The default name is “Logistic Regression”.
- Regularization type (either L1 or L2). Set the cost strength (default is C=1).
- Press Apply to commit changes. If Apply Automatically is ticked, changes will be communicated automatically.
Contoh
The widget is used just as any other widget for inducing a classifier. This is an example demonstrating prediction results with logistic regression on the hayes-roth dataset. We first load hayes-roth_learn in the File widget and pass the data to Logistic Regression. Then we pass the trained model to Predictions.
Now we want to predict class value on a new dataset. We load hayes-roth_test in the second File widget and connect it to Predictions. We can now observe class values predicted with Logistic Regression directly in Predictions.
Contoh Workflow lain,
