Logistic Regression

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.

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 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.