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  7. Random Forest Feature ImportanceRandom Forest Feature Importance
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Random Forest Feature Importance

20211229 Pikairos Feature Selection for Random Forest Classification

Nodes

  • Partitioning6 ×
  • ROC Curve6 ×
  • Scorer6 ×
  • Column Filter4 ×
  • CSV Reader3 ×
  • Number To String3 ×
  • Table Reader3 ×
  • Cell Splitter2 ×
  • Constant Value Column2 ×
  • Decision Tree Learner2 ×
  • Decision Tree Predictor2 ×
  • Decision Tree to Ruleset2 ×
  • Double To Int2 ×
  • GroupBy2 ×
  • Interactive Table2 ×
  • Round Double2 ×
  • Random Forest Learner2 ×
  • Random Forest Learner (Regression)2 ×
  • Random Forest Predictor2 ×
  • Random Forest Predictor (Regression)2 ×
  • Reference Column Filter2 ×
  • Row Filter2 ×
  • RowID2 ×
  • Rule-based Row Splitter2 ×
  • Sorter2 ×
  • Transpose2 ×
  • Unpivoting2 ×

Extensions

  • FeatureKNIME Base nodes
  • FeatureKNIME Ensemble Learning Wrappers
  • FeatureKNIME Javasnippet

Links

  • Feature Selection for Random Forest Classification
    forum.knime.com/t/feature-selection-for-random-forest-classification/38426/4

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: Muhammad Haroon
Created at: 2021-10-07
On NodePit since: 2024-10-14
Last update: 2025-05-21
Created with KNIME version: v5.3.2

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