Sklearn Logistic RegressionInstall

Summary

Predict class probabilities for each input record using a trained logistic 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 (for predict) or 'class_probabilities' mapping (for predictProba)

Requires modelData to be a valid JSON model; feature columns must exactly match training feature names; returns per-item predictions and probabilities

Examples

Example 1: Add probability predictions to a workflow that already trained a logistic regression model

modelData = JSON of trained model, predictFeatureColumns = 'feature1,feature2'

Example 2: Compute probabilities for a new dataset and pass results downstream

modelData = JSON string, predictFeatureColumns = 'age,income', pythonPath = '/usr/bin/python3'