This is documentation for Orange 2.7. For the latest documentation, see Orange 3.

Logistic Regression Learner

Logistic Regression Learner



  • Data

    A table with training instances


  • Learner

    The logistic regression learning algorithm with settings as specified in the dialog.

  • Logistic Regression Classifier

    Trained classifier (a subtype of Classifier)

Signal Logistic Regression Classifier sends data only if the learning data (signal Data) is present.


This widget provides a graphical interface to the logistic regression classifier.

As all widgets for classification, this widget provides a learner and classifier on the output. Learner is a learning algorithm with settings as specified by the user. It can be fed into widgets for testing learners, for instance Test Learners. Classifier is a logistic regression classifier (a subtype of a general classifier), built from the training examples on the input. If examples are not given, there is no classifier on the output.

Logistic Regression Widget
  1. Learner can be given a name under which it will appear in, say, Test Learners. The default name is “Logistic regression”.
  2. Set the regularization type (L1 or L2 weight penalty).
  3. Set error cost paramter (higher cost means less regularization).
  4. Normalize the features before training.


The widget is used just as any other widget for inducing classifier. See, for instance, the example for the Naive Bayesian Learner.