Hub
  • Software
  • Blog
  • Forum
  • Events
  • Documentation
  • About KNIME
Sign in
  • KNIME Hub
  • Nodes
  • AdditiveRegression (3.7)
NodeNode / Learner

AdditiveRegression (3.7)

Meta classifier that enhances the performance of a regression base classifier

Each iteration fits a model to the residuals left by the classifier on the previous iteration.Prediction is accomplished by adding the predictions of each classifier.

Reducing the shrinkage (learning rate) parameter helps prevent overfitting and has a smoothing effect but increases the learning time.

For more information see:

J.H. Friedman (1999). Stochastic Gradient Boosting.

(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.

Node details

Input ports
  1. Training data Type: Data
    Training data
Output ports
  1. Trained model Type: Weka 3.7 Classifier
    Trained model

Related workflows & nodes

    Extension

    This node is part of the extension

    KNIME Weka Data Mining Integration (3.7)Trusted extension
    Version 4.3.0
    Short link

    KNIME
    Open for Innovation

    KNIME AG
    Hardturmstrasse 66
    8005 Zurich, Switzerland
    • Software
    • Getting started
    • Documentation
    • E-Learning course
    • Solutions
    • KNIME Hub
    • KNIME Forum
    • Blog
    • Events
    • Partner
    • Developers
    • KNIME Home
    • KNIME Open Source Story
    • Careers
    • Contact us
    Download KNIME Analytics Platform Read more on KNIME Server
    © 2021 KNIME AG. All rights reserved.
    • Trademarks
    • Imprint
    • Privacy
    • Terms & Conditions
    • Credits