Logistic Regression Learner¶
Logistic Regression Learner
- Examples (ExampleTable)
A table with training examples
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 Examples 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.
The widget requires - due to limitations of the learning algorithm - data with binary class.
Learner can be given a name under which it will appear in, say, Test Learners. The default name is “Logistic Regression”.
If Stepwise attribute selection is checked, the learner will iteratively add and remove the attributes, one at a time, based on their significance. The thresholds for addition and removal of the attribute are set in Add threshold and Remove threshold. It is also possible to limit the total number of attributes in the model.
Independent of these settings, the learner will always remove singular attributes, for instance the constant attributes or those which can be expressed as a linear combination of other attributes.
Logistic regression has no internal mechanism for dealing with missing values. These thus need to be imputed. The widget offers a number of options: it can impute the average value of the attribute, its minimum and maximum or train a model to predict the attribute’s values based on values of other attributes. It can also remove the examples with missing values.
Note that there also exist a separate widget for missing data imputation, Impute.