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Sklearn Logistic Regression

Perform logistic regression classification using scikit-learn

Actions3

Train

AI-generated

Summary

Train a logistic regression classifier using scikit‑learn on the specified feature and target columns.

Inputs

  • featureColumns (required) — Comma-separated list of feature column names (X variables)
  • targetColumn (required) — Name of the target column (Y variable - class labels)
  • solver — Algorithm to use for optimization
  • maxIter — Maximum number of iterations for solver convergence
  • regularization — Inverse of regularization strength. Smaller values = stronger regularization.
  • pythonPath — Path to Python executable with scikit-learn installed

Output shape

a single record containing the trained model metadata

The record includes coefficients, intercept, classes, accuracy, feature_columns, and training_samples; it can be referenced in subsequent predict or predictProba operations.

Examples

Example 1: Train a model on a dataset with features feature1,feature2,feature3 and target column target

featureColumns=feature1,feature2,feature3; targetColumn=target; solver=lbfgs; maxIter=100; regularization=1

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