Sklearn Elastic NetInstall

Summary

Trains an Elastic Net regression model on the given feature and target columns.

Inputs

• featureColumns (required) — Comma‑separated list of feature column names
• targetColumn (required) — Name of the target column
• alpha — Regularization strength (higher = more regularization)
• l1Ratio — Mix ratio between L1 and L2 (0=Ridge, 1=Lasso, 0.5=balanced)
• maxIter — Maximum number of iterations
• pythonPath — Path to Python executable with scikit‑learn installed

Output shape

a list of items

each item includes the base64‑encoded trained model, R² score, coefficients, intercept, number of iterations, alpha, l1_ratio, and feature_columns

Examples

Example 1: Train a model to predict 'salary' from 'age', 'income', and 'experience'.

featureColumns: 'age,income,experience', targetColumn: 'salary', alpha: 1, l1Ratio: 0.5, maxIter: 1000