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Sklearn Normalizer

Normalize samples individually to unit norm using scikit-learn

Sklearn Normalizer

AI-generated

Summary

Normalize selected feature columns using scikit-learn's Normalizer.

Inputs

  • featureColumns (required) — Comma-separated list of feature column names
  • norm — Norm to use for normalization (L2, L1, Max)
  • pythonPath — Path to Python executable with scikit-learn installed

Output shape

a list of items, each appended with normalized columns

Each output item includes the original JSON plus new fields _normalized; the operation fails only if a feature column is missing unless continueOnFail is true

Examples

Example 1: Normalize three features in a dataset

featureColumns = feature1,feature2,feature3; norm = l2; pythonPath = python3

Example 2: Apply L1 norm to a single record

featureColumns = value; norm = l1

Example 3: Use a virtual environment Python interpreter

featureColumns = x,y; norm = max; pythonPath = /venv/bin/python

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