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](https://en.wikipedia.org/wiki/Logistic_regression) model from the data. It only works for classification tasks.
![](images/LogisticRegression-stamped.png)
1. A name under which the learner appears in other widgets. The default name is "Logistic Regression".
2. [Regularization](https://en.wikipedia.org/wiki/Regularization_(mathematics)) type (either [L1](https://en.wikipedia.org/wiki/Least_squares#Lasso_method) or [L2](https://en.wikipedia.org/wiki/Tikhonov_regularization)). 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
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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](../data/file.md) widget and pass the data to **Logistic Regression**. Then we pass the trained model to [Predictions](../evaluation/predictions.md).
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**.
![](images/LogisticRegression-classification.png)