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Algorithm improvements
- Switch to residual learning (predict corrections to baseline reconstructions)
- Add target standardization for balanced multi-target training
- Introduce energy-bin weighting with low-statistics suppression
- Refine XGBoost training (regularization, early stopping, updated hyperparameters)
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New features
- Training diagnostics with cached metrics (generalization gap, residual normality)
- SHAP feature importance caching per target
- Diagnostic scripts and CLI tools for evaluation and interpretability
- Reproducible diagnostics via model metadata reconstruction
- Expanded test suite and improved error handling
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Bug fixes
- Correct log10 handling for energy residuals
- Fix scaler loading/inversion in apply pipeline
- Fix energy-bin weighting logic
- Ensure safe energy validation (ErecS) without dropping rows
- Align evaluation metrics with residual formulation
- Resolve pandas/sklearn warnings and compatibility issues