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Outlier Detection

data manipulation preprocessing outlier detection z-score DBSCAN +2

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Introduction to Machine Learning Algorithms course - Session 4 Exercise 2 Detect and remove outliers in the data using the following techniques: - Numeric outliers outside the upper/lower whiskers of a box plot - Outliers in the distribution tails (z-score) - Outliers remote from cluster centers (DBSCAN)

External resources

  • Slides (Introduction to ML Algorithms course)
  • Four Techniques for Outlier Detection
  • Description of the Ames Iowa Housing Data
  • Ames Housing Dataset on kaggle

Used extensions & nodes

Created with KNIME Analytics Platform version 4.3.0
  • KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.0

  • KNIME Javasnippet Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.0

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