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Sklearn Elastic Net

Elastic Net regression (L1 + L2 regularization) using scikit-learn

Actions2

Predict

AI-generated

Summary

Predict upcoming target values using a trained Elastic Net model.

Inputs

  • modelData (required) — JSON string containing the trained model (base64‑encoded pickle) produced by the Train operation.
  • predictFeatureColumns (required) — Comma‑separated list of feature column names to use for prediction.
  • pythonPath — Path to the Python executable with scikit‑learn installed; defaults to 'python3'.

Output shape

An array of items, each extending the input record with a 'prediction' field holding the model's numeric predictions.

The predictions list length matches the number of input items; errors are returned as separate error items when continueOnFail is false.

Examples

Example 1: Apply a previously trained model to predict on a dataset

modelData: '{"model_pickle":"…base64…"}', predictFeatureColumns: 'feature1,feature2,feature3'

Example 2: Predict on a single workflow item with three features

modelData: '', predictFeatureColumns: 'age,income,score'

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