Quality Metrics for Principal Components Analysis
- At least the largest eigenvalue is non-zero. Returns an error otherwise.
- The number of eigenvaluespmust be equal to the number of features provided. Returns an error ifpis less than the number of features.
- Explained variance ratio
- Noise variance
Explained variance ratios
peigenvalues (explained variances), numeric table of size .
You can define it as an object of any class derived from
The floating-point type that the algorithm uses for intermediate computations. Can be
The number of principal components to compute metrics for. If it is zero, the algorithm will compute the result for
The number of features in the data set used as input in PCA algorithm. If it is zero, the algorithm will compute the result for p.
if , the algorithm will return non-relevant results.
Pointer to the numeric table that contains a reduced eigenvalues array.
Pointer to the numeric table that contains an array of reduced explained variances ratios.
Pointer to the numeric table that contains noise variance.