Sklearn Standard ScalerInstall

Summary

Fit a StandardScaler on specified feature columns and immediately transform the data, adding scaled columns with a configurable prefix.

Inputs

• featureColumns (required) — Comma‑separated list of column names to scale (e.g., 'age,income').
• withMean — Whether to center the data before scaling. Defaults to true.
• withStd — Whether to scale the data to unit variance. Defaults to true.
• outputPrefix — String prefix added to each scaled column name. Defaults to 'scaled_'.
• pythonPath — Path to the Python executable that has scikit‑learn installed. Defaults to 'python3'.

Output shape

Array of items, each containing the original fields plus new scaled columns.

For each input item, new columns named are added with the scaled values. The first item also receives two metadata fields: scaler (a JSON string representing the fitted scaler) and scaler_info (an object with mean, scale, and variance arrays).

Examples

Example 1: Standardize 'age' and 'salary' columns.

Set Feature Columns to 'age,salary', With Mean and With Std to true, Output Prefix to 'scaled_'.