MLE-STAR Targeted Refinement Plan

Agent: Refinement Specialist

• Agent ID: refinement_agent
• Session ID: automation-session-1754319839721-scewi2uw3
• Execution ID: workflow-exec-1754319839721-454uw778d
• Timestamp: 2025-08-04T15:05:33Z

Phase: Targeted Component Optimization

Configured Capabilities:

1. Feature Engineering - Advanced feature transformation and creation
2. Model Optimization - Hyperparameter tuning and architecture improvements
3. Hyperparameter Tuning - Grid search, Bayesian, and evolutionary optimization
4. Ablation Analysis - Component impact assessment

Optimization Methods Available:

• Grid Search
• Bayesian Optimization
• Evolutionary Algorithms

Refinement Strategy

1. Component Impact Analysis

I will perform systematic ablation analysis to identify which pipeline components have the highest impact on performance:

Components to Analyze:

• Data Preprocessing Pipeline

  • Scaling methods (StandardScaler vs MinMaxScaler vs RobustScaler)
  • Missing value imputation strategies
  • Outlier detection and handling

• Feature Engineering

  • Polynomial features
  • Feature interactions
  • Domain-specific feature creation
  • Feature selection methods (RFE, L1 regularization, mutual information)

• Model Architecture

  • Base model selection (linear, tree-based, neural)
  • Ensemble strategies
  • Model complexity vs performance trade-off

• Hyperparameter Configuration

  • Learning rates
  • Regularization parameters
  • Tree depths / layers
  • Optimization algorithms

2. Iterative Refinement Process

Step 1: Baseline Performance Assessment

• Establish current performance metrics
• Identify performance bottlenecks
• Document baseline configurations

Step 2: Component-wise Ablation

• Remove/modify one component at a time
• Measure performance impact
• Rank components by impact score

Step 3: Targeted Deep Optimization

• Focus on top 3 high-impact components
• Apply advanced optimization techniques:
  • Bayesian optimization for continuous hyperparameters
  • Evolutionary algorithms for discrete choices
  • Grid search for exhaustive small spaces

Step 4: Feature Engineering Enhancement

• Create interaction features for high-importance variables
• Apply domain-specific transformations
• Implement automated feature selection

Step 5: Ensemble Optimization

• Optimize base model diversity
• Tune ensemble weights using cross-validation
• Implement stacking with meta-learner

3. Performance Tracking

All refinements will be tracked with:

• Performance delta from baseline
• Computational cost
• Model complexity metrics
• Cross-validation stability

4. Coordination Protocol

As per MLE-STAR requirements, I will:

• Store all decisions in memory using npx claude-flow@alpha memory store
• Log progress after each major step with hooks
• Share findings with other agents through the memory system
• Maintain full audit trail of optimizations

Next Steps

1. Create ablation analysis framework
2. Implement component impact measurement
3. Execute targeted optimizations on high-impact components
4. Document and share results through coordination system