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Outlier Detection

Data manipulation Preprocessing Outlier detection Z-score DBSCAN
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Introduction to Machine Learning Algorithms course - Session 4 Exercise 3 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

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

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Created with KNIME Analytics Platform version 4.4.0
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    KNIME AG, Zurich, Switzerland

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