LMT (3.7)

Learner

Classifier for building 'logistic model trees', which are classification trees with logistic regression functions at the leaves

The algorithm can deal with binary and multi-class target variables, numeric and nominal attributes and missing values.

For more information see:

Niels Landwehr, Mark Hall, Eibe Frank (2005).Logistic Model Trees.

Machine Learning.95(1-2):161-205.

Marc Sumner, Eibe Frank, Mark Hall: Speeding up Logistic Model Tree Induction.

In: 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, 675-683, 2005.

(based on WEKA 3.7)

For further options, click the 'More' - button in the dialog.

All weka dialogs have a panel where you can specify classifier-specific parameters.

Input Ports

  1. Type: Data Training data

Output Ports

  1. Type: Weka 3.7 Classifier Trained model

Find here

Analytics > Mining > Weka > Weka (3.7) > Classification Algorithms > trees

Make sure to have this extension installed:

KNIME Weka Data Mining Integration (3.7)

Update site for KNIME Analytics Platform 3.7:
KNIME Analytics Platform 3.7 Update Site

How to install extensions