Sklearn MinMax Scaler icon

Sklearn MinMax Scaler

Scale features to a given range using scikit-learn MinMaxScaler

Actions4

Transform

AI-generated

Summary

Transforms input data using a previously fitted MinMax scaler, adding scaled columns with a configurable prefix.

Inputs

  • scalerData (required) — JSON string containing the fitted scaler (from a prior Fit operation).
  • transformFeatureColumns (required) — Comma‑separated list of feature column names to scale.
  • outputPrefix — Prefix added to scaled column names. Defaults to 'scaled_'.
  • pythonPath — Path to the Python executable with scikit‑learn installed. Defaults to 'python3'.

Output shape

Each input item is returned with additional columns named <outputPrefix><feature> containing the scaled values; all original columns are preserved.

If a specified feature column is missing or non‑numeric, the node throws an error.

Examples

Example 1: Scale 'age' and 'income' columns using a scaler obtained from a previous Fit node.

Set scalerData to the JSON from the Fit node, transformFeatureColumns to 'age,income', outputPrefix to 'scaled_' (default).

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