The following node is available in the Open Source KNIME predictive analytics and data mining platform version 2.7.1. Discover over 1000 other nodes, as well as enterprise functionality at http://knime.com.

Logistic Regression (Learner)

Performs a multinomial logistic regression. Select in the dialog a target column (combo box on top), i.e. the response. The two lists in the center of the dialog allow you to include only certain columns which represent the (independent) variables. Make sure the columns you want to have included being in the right "include" list. See article in wikipedia about logistic regression for an overview about the topic. This particular implementation uses an iterative optimization procedure termed Fisher's scoring in order to compute the model.
If the optional PMML inport is connected and contains preprocessing operations in the TransformationDictionary those are added to the learned model.

Dialog Options

Target
To select the target column. Only columns with nominal data are allowed.
Values
To specify the independent columns the should be included in the regression model. Numeric and nominal data can be included.

Ports

Input Ports
0 Table on which to perform regression. The input must not contain missing values, you have to fix them by e.g. using the Missing Values node.
1 Optional PMML port object containing preprocessing operations.
Output Ports
0 Model to connect to a predictor node.
1 Coefficients and statistics of the logistic regression model.

Views

Logistic Regression Result View
Displays the estimated coefficients and error statistics. Note, that the estimated coefficients are not reliable when the standard error is high.
This node is contained in KNIME Base Nodes provided by KNIME GmbH, Konstanz, Germany.