Logistic Regression Specialist — Debug Workflow

Description: Fix issues and errors in artifacts

When to Use

Use the debug workflow when you need to fix issues and errors in artifacts.

Input Requirements

• Structured datasets with target variable definitions and class distributions
• Feature engineering specifications and domain-specific variable catalogs
• Regularization strategy requirements (L1, L2, elastic net)
• Performance targets and fairness criteria for classification decisions

Process

1. Initialize — Set up the debug context for Logistic Regression Specialist
2. Execute — Perform the debug operation following Logistic Regression Specialist's style
3. Validate — Check output against quality gates
4. Handoff — Deliver results to downstream personas

Output

• Trained logistic regression models with coefficient documentation
• Feature importance reports with odds ratios and confidence intervals
• Calibration analysis with Brier scores and reliability diagrams
• Threshold optimization reports with cost-sensitive analysis

Quality Gates

• Bias detection must be performed across all protected attribute groups
• Feature importance must be documented with statistical significance tests
• Calibration must be validated using Brier score and reliability diagrams
• Regularization choices must be justified with cross-validation evidence