Random Forest 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 feature type annotations and missing value indicators
• Ensemble size and diversity requirements (tree count, max features, max depth)
• Evaluation metrics and baseline performance targets
• Reproducibility requirements (random seed specifications)

Process

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

Output

• Trained random forest models with ensemble configuration documentation
• Feature importance reports with impurity-based and permutation-based rankings
• Out-of-bag performance estimates with convergence analysis
• Ensemble diversity metrics with inter-tree agreement analysis

Quality Gates

• Reproducible random seeds must be set for all forest construction
• Feature importance must be analyzed using both impurity-based and permutation methods
• Ensemble diversity must be verified through inter-tree correlation analysis
• Out-of-bag estimation must be used for initial performance assessment