# 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.

- 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.

## 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**.