# 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