Sift Tutorial: Outlier Detection with PCA: Difference between revisions
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Revision as of 19:08, 30 April 2024
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This tutorial will show you how to use the outlier detection methods built into Sift, when each method might be appropriate and how to interpret the results. Outlier detection is a key artifact of data analysis, identifying errant data or anomalies that might make further data analysis less effective. Within Sift, there are multiple methods for detecting outliers, but we will be focusing on doing so with PCA data in this tutorial.
Data
(HipAngleX - Traces: 2 distinct groups and several outliers)