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Data Preprocessing for ML Models

Preprocessing Partitioning Outlier detection Missing value PCA
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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

Used extensions & nodes

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

    KNIME AG, Zurich, Switzerland

    Version 4.4.2

  • KNIME Ensemble Learning Wrappers Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

  • KNIME JavaScript Views Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.2

  • KNIME Javasnippet Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.2

  • KNIME Math Expression (JEP) Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

  • KNIME PMML Preprocessing Applier Nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

  • KNIME Statistics Nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

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