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Sklearn PCA

Principal Component Analysis using scikit-learn

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Fit

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Summary

Fit PCA model on specified feature columns.

Inputs

  • featureColumns (required) — Comma-separated list of feature column names to use for PCA.
  • nComponents — Number of principal components to retain; set 0 to keep all components.
  • pythonPath — Path to the Python executable that has scikit-learn installed.

Output shape

a list of items; each item contains fitted PCA metadata

The first item includes the fitted model JSON, number of components, explained variance ratios, cumulative variance ratios, feature columns, and fitted sample count; no transformed data is produced.

Examples

Example 1: Fit PCA on three feature columns

Feature Columns = 'feature1,feature2,feature3', Number of Components = 2, Python Path = 'python3'

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