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KNIME_​project

Example Data Reader
Random Forest Predictor
Source dataset containing operational shipment records (Distance, transit times costs, disruption indicators)
CSV Reader
Applies trained Random forest model to un seen validation data to estimate disruption risk
Random Forest Predictor
Trains ensemble Decision tree classifier to predict shipment route disruption risk
Random Forest Learner
Generates confusion matrix and classification accuracy metrics
Scorer
Randomly partitions labelled dataset into training (70%) and Validation (30%)
Table Partitioner
Creates binary route risk class (High/Low) based on delays threshold logic
Rule Engine
Validates class Balance acroos HIGH and LOW risk categories prior to model training
Value Counter

Nodes

Extensions

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

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Created by: steph
Created at: 2025-12-15
On NodePit since: 2025-12-16
Last update: 2026-01-30
Created with KNIME version: v5.9.0

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