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Sklearn One Hot Encoder

One-hot encode categorical features using scikit-learn

Actions2

Transform

AI-generated

Summary

Fit or transform categorical columns using a scikit-learn OneHotEncoder.

Inputs

  • encoderData (required) — JSON string containing the fitted encoder
  • transformColumns (required) — Comma-separated list of columns to transform
  • pythonPath — Path to Python executable with scikit-learn installed

Output shape

An array of workflow items that extend each input record with encoded columns and metadata.

Requires a valid fitted encoder in encoderData; transformColumns must match the encoder's columns; handleUnknown can be 'error' or 'ignore'; output includes 'encoded', 'feature_names', 'categories', 'drop_first', and 'handle_unknown' fields.

Examples

Example 1: Fit new encoder and transform data

operation: fitTransform; columns: color,size; dropFirst: false; handleUnknown: error

Example 2: Transform using existing encoder

operation: transform; encoderData: {"json":"..."}; transformColumns: "color,size"; handleUnknown: ignore

Example 3: Fit transform with drop first enabled

operation: fitTransform; columns: category; dropFirst: true; handleUnknown: error

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