1.0 - 8.00 - PCA Output - Teradata Vantage

Teradata® Vantage Machine Learning Engine Analytic Function Reference

Teradata Vantage
Release Number
Release Date
May 2019
Content Type
Programming Reference
Publication ID
English (United States)

PCAReduce Output Table Schema

Each row represents a principal component. The rows are in descending order based on the standard deviation, which is a measure of the variation in the data set that was captured by that principal component.

Column Data Type Description
component_rank INTEGER Rank of principal component. Components are ranked in descending order of standard deviation (and variance).
dimension_i DOUBLE PRECISION [Column appears once for each dimension.] Values of ith dimension of data set.
sd DOUBLE PRECISION Standard deviation of components in eigenvector represented by row.
var_proportion DOUBLE PRECISION Proportion of variance of components in eigenvector represented by row.
cumulative_var DOUBLE PRECISION Cumulative variance of components in eigenvector represented by row.
mean VARCHAR One row of this column contains a list of average values, one for each target_column_i in the input table. The list has this format:

[average [, average ...]]

The outer brackets appear in the table.

The other rows of this column contain NULL.