Hub
Pricing About
  • Software
  • Blog
  • Forum
  • Events
  • Documentation
  • About KNIME
  • KNIME Community Hub
  • knime
  • Spaces
  • Examples
  • 04_Analytics
  • 01_Preprocessing
  • 04_Data_Preprocessing_for_ML_Models
WorkflowWorkflow

Data Preprocessing for ML Models

Preprocessing Partitioning Outlier detection Missing value PCA
+5
KNIME profile image

Last edit:

Drag & drop
Like
Download workflow
Copy short link
Workflow preview
This workflow demonstrates the following standard preprocessing steps before training a machine learning model: - Partitioning - Outlier detection - Missing value handling - Dimensionality reduction - Conversion - Feature selection

External resources

  • House Prices Dataset

Used extensions & nodes

Created with KNIME Analytics Platform version 4.4.2
  • Go to item
    KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.2

    KNIME profile image
    knime
  • Go to item
    KNIME Ensemble Learning Wrappers Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

    KNIME profile image
    knime
  • Go to item
    KNIME JavaScript Views Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.2

    KNIME profile image
    knime
  • Go to item
    KNIME Javasnippet Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.2

    KNIME profile image
    knime
  • Go to item
    KNIME Math Expression (JEP) Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

    KNIME profile image
    knime
  • Go to item
    KNIME PMML Preprocessing Applier Nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

    KNIME profile image
    knime
  • Go to item
    KNIME Statistics Nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

    KNIME profile image
    knime
  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