Sklearn Linear RegressionInstall

Summary

Predict target values using a trained scikit‑learn linear regression model.

Inputs

• modelData (required) — JSON string containing the trained model (from train operation)
• predictFeatureColumns (required) — Comma-separated list of feature column names (must match training features)
• pythonPath — Path to Python executable with scikit-learn installed

Output shape

a list of items, each containing a 'prediction' field

prediction is a numeric value; if Python execution fails and continueOnFail is enabled, an 'error' field is included in the output item

Examples

Example 1: Predict house price from a trained model

Model Data: '', PredictFeatureColumns: 'size,rooms', Python Path: default

Example 2: Batch predict on a CSV export

Model Data: '', PredictFeatureColumns: 'age,income,score', Python Path: /usr/bin/python3