# 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.

- 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* 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**.