Hub
Pricing About
NodeNode / LoopEnd

Boosting Learner Loop End

AnalyticsMiningEnsemble Learning
Drag & drop
Like

Together with the corresponding loop start node a boosting loop can be constructed. It repeatedly trains simple models and weighs them according to their classification error. The algorithm used is AdaBoost.SAMME, i.e. is can also cope with multi-class problems. The loop is stopped either after the maximum number of iterations has been reached or the weight for a model is only slightly above 0 (meaning the prediction error is too big).

Node details

Input ports
  1. Type: PortObject
    Model
    The trained model
  2. Type: Table
    Predicted data
    The data with predicted classes and also the real class values
Output ports
  1. Type: Table
    Boosting model
    The boosted models together with their weights in data table

Extension

The Boosting Learner Loop End node is part of this extension:

  1. Go to item

Related workflows & nodes

  1. Go to item
  2. Go to item
  3. Go to item

KNIME
Open for Innovation

KNIME AG
Talacker 50
8001 Zurich, Switzerland
  • Software
  • Getting started
  • Documentation
  • Courses + Certification
  • Solutions
  • KNIME Hub
  • KNIME Forum
  • Blog
  • Events
  • Partner
  • Developers
  • KNIME Home
  • Careers
  • Contact us
Download KNIME Analytics Platform Read more about KNIME Business Hub
© 2025 KNIME AG. All rights reserved.
  • Trademarks
  • Imprint
  • Privacy
  • Terms & Conditions
  • Data Processing Agreement
  • Credits