Logistic Regression

The logistic regression classification algorithm with LASSO (L1) or ridge (L2) regularization.

Signals

Inputs:

• Data

  A data set

• Preprocessor

  Preprocessing method(s)

Outputs:

• Learner

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

• Logistic Regression Classifier

  A trained classifier. Output signal sent only if input Data is present.

Description

Logistic Regression learns a Logistic Regression model from the data.

It only works for classification tasks.

1. A name under which the learner appears in other widgets. The default name is “Logistic Regression”.
2. Regularization type (either L1 or L2). Set the cost strength (default is C=1).
3. 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 data set. 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 data set. 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.