The logistic regression classification algorithm with LASSO (L1) or ridge (L2) regularization.
- Data: input dataset
- Preprocessor: preprocessing method(s)
- 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.
Logistic Regression uses default preprocessing when no other preprocessors are given. It executes them in the following order:
- removes instances with unknown target values
- continuizes categorical variables (with one-hot-encoding)
- removes empty columns
- imputes missing values with mean values
To remove default preprocessing, connect an empty Preprocess widget to the learner.
Logistic Regression can be used with Rank for feature scoring. See Learners as Scorers for an 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.