Hub
  • Software
  • Blog
  • Forum
  • Events
  • Documentation
  • About KNIME
  • KNIME Hub
  • knime
  • Spaces
  • Examples
  • 04_Analytics
  • 04_Classification_and_Predictive_Modelling
  • 10_Analyzing_Churn_Models_with_the_Binary_Classification_Inspector
WorkflowWorkflow

Analyzing Churn Models with the Binary Classification Inspector

Machine Learning Classification Data Mining Decision Tree Binary Classification Inspector
+5

Last edited: 

Drag & drop
Like
Download workflow
Copy short link
Workflow preview
This workflow demonstrates the functionality of the Binary Classification Inspector node. It produces a complex view made of four different charts in order to compare, optimize and select predictions of different binary classifiers. It is possible to compare a number of binary classifier machine learning models predicting the same target on the same test data using performance metrics and ROC curves. Here four machine learning models are used: Naive Bayes, Random Forest, Gradient Boosted Trees, Logistic Regression and Decision Tree. By moving a threshold slider in the interactive view you can optimize a model by finding the best threshold given a performance metric of your choice. It is possible to interactively select a given type of predictions (e.g. true positives) of one of the models and export them at the output of the node

Used extensions & nodes

Created with KNIME Analytics Platform version 4.1.0
  • Go to item
    KNIME Core Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.1.0

  • Go to item
    KNIME Excel Support Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.1.0

  • Go to item
    KNIME Machine Learning Interpretability Extension Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.1.0

  1. Go to item
  2. Go to item
  3. Go to item
  4. Go to item
  5. Go to item
  6. Go to item

Legal

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

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
© 2022 KNIME AG. All rights reserved.
  • Trademarks
  • Imprint
  • Privacy
  • Terms & Conditions
  • Credits