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Deden Rafi AKbar _​Tugas-8 IMEL-A

Training and Testing a Model (decision tree)

This workflow shows how to train and test a basic classification model. Using the adult dataset, a decision tree is trained and tested to predict the "income" class column. Testing is obtained via simple accuracy measures via the Scorer node, the ROC curve, and a Cross Validation loop.

Train decision tree to predict income class
Decision Tree Learner
ROC Curve
Predict the testing data
Decision Tree Predictor
Show confusion matrix and accuracy statistics
Scorer
Table Partitioner
Read the adult dataset
CSV Reader

Nodes

Extensions

Links

Download

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

Download Workflow
Created by: kathrinmelcher
Created at: 2017-09-14
On NodePit since: 2026-04-23
Last update: 2026-04-23
Created with KNIME version: v5.11.0
Tags: TheGuideBookdecision treetestingtrainingcross-validationscoringaccuracyCohen's KappasensitivityspecificityprecisionrecallROCGIDS

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