Principal Component Analysis Predict VAL

The Predict function generates PCA scores using the model created by PCA VALIB function. The scoring process expresses each component as a linear combination of the input columns. There is an additional required input table that specifies the model used by the VALIB PCA function.

Options

Accumulate: Specifies one or more columns from the 'data' DataFrame that can be passed to the result output DataFrame.
Index Columns: Specifies one or more different columns for the primary index of the result output DataFrame.
Output Schema: Output Schema, if Volatile is true then use user login as the schema.
Output Table: Output Table
VAL Location: VAL Location

Input Ports

: Connection to a Teradata Database Instance
: Principal Component Analysis Predict VAL input
: Principal Component Analysis Predict VAL input

Output Ports

: Principal Component Analysis Predict VAL output

Nodes

String Configuration3 ×
Column Filter Configuration2 ×
Python Script2 ×
Table Row to Variable2 ×
Component Input1 ×
Component Output1 ×
DB Data Spec Extractor1 ×
DB SQL Executor (Labs)1 ×
DB Table Remover (Labs)1 ×
DB Table Selector (Labs)1 ×
Merge Variables1 ×
Transpose1 ×

Extensions

FeatureKNIME Base nodes
FeatureKNIME Database
FeatureKNIME Quick Forms