Difference between revisions of "Orange: Logistic Regression"

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Sumber: https://docs.biolab.si//3/visual-programming/widgets/model/logisticregression.html
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The logistic regression classification algorithm with LASSO (L1) or ridge (L2) regularization.
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Inputs
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    Data: input dataset
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    Preprocessor: preprocessing method(s)
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Outputs
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    Learner: logistic regression learning algorithm
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    Model: trained model
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    Coefficients: logistic regression coefficients
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Logistic Regression learns a Logistic Regression model from the data. It only works for classification tasks.
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../../_images/LogisticRegression-stamped.png
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    A name under which the learner appears in other widgets. The default name is “Logistic Regression”.
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    Regularization type (either L1 or L2). Set the cost strength (default is C=1).
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    Press Apply to commit changes. If Apply Automatically is ticked, changes will be communicated automatically.
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Example
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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.
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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.
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../../_images/LogisticRegression-classification.png
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[[File:OrangeLogisticRegression.png|center|400px|thumb]]
 
[[File:OrangeLogisticRegression.png|center|400px|thumb]]
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==Referensi==
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* https://docs.biolab.si//3/visual-programming/widgets/model/logisticregression.html
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==Pranala Menarik==
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* [[Orange]]

Revision as of 20:53, 12 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.

Inputs

   Data: input dataset
   Preprocessor: preprocessing method(s)

Outputs

   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.

../../_images/LogisticRegression-stamped.png

   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.

Example

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.

../../_images/LogisticRegression-classification.png


OrangeLogisticRegression.png


Referensi

Pranala Menarik