Optimize Hyperparameters

1. Objective

To execute Optimize Hyperparameters for high-performing AI systems.

2. When to use / When not to use

When to use:

• When working on optimize hyperparameters.

When not to use:

• Without sufficient data.

3. Inputs (Required/Optional)

Required:

• Training Data
• Model Config

Optional:

• Relevant documentation

4. Outputs (Artifacts)

• Model Artifact: Description of the Model Artifact artifact.
• Metrics Report: Description of the Metrics Report artifact.

5. Operating Modes

Mode	Description	Verification Level
Fast	Focus on speed, minimal validation.	Basic syntax/lint checks only.
Standard	Balanced approach.	Unit tests and standard linting.
Deep	Comprehensive analysis and optimization.	Full test suite, performance profiling, security scan.

6. Constraints & Guardrails

• No Broken Builds: Ensure all changes pass the build process.
• Code Style: Strictly adhere to the project's linting and formatting rules.
• Security: Do not introduce new vulnerabilities; sanitize all inputs.
• Performance: Avoid O(n^2) or worse complexity unless strictly necessary and documented.
• Testing: Maintain or improve code coverage; do not degrade it.

7. Procedure

Phase 1: Data Prep

1. Clean data.
2. Split train/test.
3. Normalize features.

Phase 2: Training

1. Configure model.
2. Run training loop.
3. Log metrics.

Phase 3: Evaluation

1. Calculate accuracy/F1.
2. Check bias.
3. Save artifact.

8. Quality Gates (Definition of Done)

• [ ] code compiles/runs without errors.
• [ ] All new components include identical or improved test coverage.
• [ ] No new linting errors or warnings introduced.
• [ ] Documentation updated (inline and external).
• [ ] Security scan passes (no high/critical severities).

9. Failure Modes & Recovery

Failure Mode	Recovery Action
Build Failure	Check error logs, revert recent changes, verify dependencies.
Test Failure	Isolate failing test, debug logic, or update test if requirements changed.
Linting Error	Run auto-formatter and manually fix remaining issues.
Merge Conflict	Rebase on main, resolve conflicts manually, run tests again.

10. Copy-Paste Prompt

Role: Act as a Senior Data Scientist.
Task: Execute the Optimize Hyperparameters workflow.

## Objective & Scope
- **Goal**: Systematically find the optimal hyperparameters for a machine learning model to maximize performance.
- **Scope**: Search Strategy (Grid/Random/Bayes), Metric Selection, Cross-Validation, and Result Analysis.

## Inputs
- [ ] MODEL: The model algorithm (e.g., Random Forest).
- [ ] DATASET: Training and Validation data.
- [ ] PARAM_GRID: Dictionary of parameters and ranges to search.
- [ ] METRIC: Target metric (e.g., F1-Score, AUC).

## Output Artifacts
- [ ] Best Parameter Set (JSON)
- [ ] Optimization Report (CV Results)
- [ ] Final Tuned Model

## Execution Steps
1. **Configure**
   - Define the search space (using Optuna or Scikit-Learn `GridSearchCV`). Set the cross-validation strategy.
2. **Search**
   - Run the optimization loop. Use early stopping if available to save resources.
3. **Analyze**
   - Extract the best parameters. Evaluate the impact of each parameter on the metric.

## Quality Gates
- [ ] Search completed successfully.
- [ ] Best hyperparameters identified.
- [ ] Performance improvement verified against baseline.
- [ ] No overfitting on validation set.

## Failure Handling
- If blocked, output a "Clarification Brief" detailing missing info or blockers.

## Constraints
- **Resource**: Respect compute and time budget.
- **Methodology**: Use K-Fold Cross-Validation to ensure robustness.

## Command
Now execute this workflow step-by-step.