Hub
Pricing About
  • Software
  • Blog
  • Forum
  • Events
  • Documentation
  • About KNIME
  • KNIME Community Hub
  • catherineoleary
  • Spaces
  • Public
  • L2-LS KNIME Analytics Platform for Data Scientists - Life Sciences - Advanced
  • Exercises
  • 03. Advanced Machine Learning Chemistry
WorkflowWorkflow

Advanced Machine Learning - Chemistry

Machine learning Data mining Classification Prediction Model evaluation
+9
Catherineoleary profile image

Last edited: 

Drag & drop
Like
Download workflow
Copy short link
Workflow preview
"Advanced Machine Learning Chemistry" exercise for the advanced Life Science User Training - Training a Random Forest model to predict a nominal target column - Evaluating the performance of a classification model - Optimizing parameters of the Random Forest model - Performing the classification multiple times in a cross validation loop

External resources

  • Parameter Optimization for Prediction Models
  • Random Forest
  • Slides for KNIME Analytics Platform Course (L2-LS)

Used extensions & nodes

Created with KNIME Analytics Platform version 4.5.2
  • Go to item
    KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.2

    knime
  • Go to item
    KNIME Ensemble Learning Wrappers Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

    knime
  • Go to item
    KNIME JavaScript Views Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.2

    knime
  • Go to item
    KNIME JavaScript Views (Labs) Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

    knime
  • Go to item
    KNIME Optimization extension Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

    knime
  • Go to item
    RDKit Nodes Feature Trusted extension

    NIBR

    Version 4.5.0

    manuelschwarze
  1. Go to item
  2. Go to item
  3. Go to item
  4. Go to item
  5. Go to item
  6. Go to item
Loading deployments
Loading ad hoc executions

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
Talacker 50
8001 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 Business Hub
© 2023 KNIME AG. All rights reserved.
  • Trademarks
  • Imprint
  • Privacy
  • Terms & Conditions
  • Credits