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- Update worker.py to support multi-model ensemble architecture (XGBoost, AttentiveFP, DimeNet++, elastic-net blender) - Replace placeholder confidence scores with variance-based confidence intervals from ensemble predictions - Add comprehensive feature validation and normalization utilities in processing.py - Extend models with uncertainty metrics and ensemble predictions fields - Update configuration to support individual model file paths - Simplify tests to focus on core feature extraction functionality
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Summary
• Add GraphQL helper functions for API communication
• Implement basic frontend implementation with Next.js
• Add XGBoost requirement to backend virtual environment
• Update backend worker for classification model compatibility
• Implement ensemble ML pipeline with calibrated confidence intervals
Test plan