Orange: Logistic Regression

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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.

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.

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.

LogisticRegression-classification.png

Contoh Workflow lain,

OrangeLogisticRegression.png


Referensi

Pranala Menarik