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Sklearn PCA

Principal Component Analysis using scikit-learn

Actions4

Transform

AI-generated

Summary

Transform data using a fitted PCA model

Inputs

  • pcaData (required) — JSON string containing the fitted PCA model
  • transformColumns (required) — Comma-separated list of feature column names (must match fitted features)
  • pythonPath — Path to Python executable with scikit-learn installed

Output shape

list of items (same length as input) with added PC columns

New columns are named PC1, PC2, etc.; the fitted PCA model is attached to the first transformed record as pca_model

Examples

Example 1: Transform a dataset using a previously fitted PCA model

pcaData: JSON string of fitted model; transformColumns: 'feature1,feature2,feature3'; pythonPath: default

Example 2: Transform CSV rows selecting specific feature columns

transformColumns set to selected column names; pcaData provided from earlier node

Example 3: Transform data with default Python path

pcaData supplied; transformColumns specified; pythonPath left as default

Discussion