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Dimensionality Reduction - solution

Dimensionality reduction Data manipulation Preprocessing PCA Feature importance
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Introduction to Machine Learning Algorithms course - Session 4 Solution to exercise 4 Apply the following dimensionality reduction techniques to the data: - Filter out columns with a low variance - Filter out one of two columns with a high linear correlation - Replace numeric columns with principal components - Filter out columns which are not important in predicting the target column

External resources

  • Slides (Introduction to ML Algorithms course)
  • 3 New Techniques for Data-Dimensionality Reduction in Machine Learning
  • Seven Techniques for Data Dimensionality Reduction
  • Description of the Ames Iowa Housing Data
  • Ames Housing Dataset on kaggle

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Created with KNIME Analytics Platform version 4.6.1
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    KNIME AG, Zurich, Switzerland

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    KNIME Statistics Nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.6.0

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