Developer Guide

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Details

The metrics are computed given the input data meets the following requirements:
  • At least the largest eigenvalue
    is non-zero. Returns error otherwise.
  • Number of eigenvalues
    p
    must be equal to the number of features provided. Returns error if
    p
    is less than the number of features.
The PCA algorithm receives input argument eigenvalues
. It represents the following quality metrics:
  • Explained variance ratio
  • Noise variance
The library uses the following quality metrics:
Quality Metric
Definition
Explained variance
Explained variance ratios
Noise variance
p
r
- number of principal components,
p
- number of features in the data set
Quality metrics for PCA are correctly calculated only if the eigenvalues vector obtained from the PCA algorithm has not been reduced. That is, the
nComponents
parameter of the PCA algorithm must be zero or equal to the number of features. The formulas rely on a full set of the principal components. If the set is reduced, the result is considered incorrect.

Product and Performance Information

1

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Notice revision #20110804