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

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](../evaluate/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)