Hub
  • Software
  • Blog
  • Forum
  • Events
  • Documentation
  • About KNIME
Sign in
  • KNIME Hub
  • knime
  • Spaces
  • Examples
  • 50_Applications
  • 39_Fraud_Detection
  • 01_Fraud_Detection_Model_Training
WorkflowWorkflow

Fraud Detection: Model Training

fraud fraud detection random forest deployment banking +2

Last update: 

Drag Workflow
Workflow preview
This workflow reads in the creditcard.csv file and trains and evaluates a Random Forest model to classify transactions as either fraudulent or not. Notice the final Rule Engine node. This node classifies all transactions with fraud probability above 0.3 as fraudulent.

Used extensions & nodes

Created with KNIME Analytics Platform version 4.3.0
  • KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.0

  • KNIME Ensemble Learning Wrappers Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.0

  • KNIME JavaScript Views (Labs) Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.0

  • KNIME Javasnippet Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.0

  • KNIME Quick Forms Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.0

Legal

By downloading the workflow, you agree to our terms and conditions.

License (CC-BY-4.0)
Short link
Discussion
Discussions are currently not available, please try again later.

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