changelog

Author: GernotMaier

docs/changes/53.feature.md

@@ -23,3 +23,15 @@
   - `plot_training_evaluation.py`: Energy resolution and residual distribution visualization
 - **Comprehensive test suites**: 20 new tests covering residual computation, standardization, energy weighting, apply inference
 - **Robust error handling**: Clear messages for missing scalers; guaranteed row-count preservation in apply pipeline
+
+### Enhanced Diagnostic Pipeline
+
+- **Generalization-gap metrics cached during training**: Train/test RMSE, gap %, and generalization ratio computed and cached in the model artifact, enabling fast overfitting assessment without recomputation
+- **Residual normality statistics cached during training**: Normality tests (Kolmogorov-Smirnov, Anderson-Darling), distribution shape metrics (skewness, kurtosis, Q-Q R²), and outlier counts computed once during training and cached for fast retrieval
+- **Diagnostic reconstruction from model metadata**: All regression diagnostics (generalization-gap, partial-dependence, residual-normality) now reconstruct the held-out test split from stored model metadata + input file list, enabling reproducibility and offline analysis without CSV exports
+- **Cache-first diagnostic workflows**: Diagnostic scripts load cached metrics first (fast) with graceful fallback to reconstruction if cache unavailable (backward compatible with older models)
+- **CLI entry points for all diagnostics**:
+  - `eventdisplay-ml-diagnostic-generalization-gap`: Quantify overfitting via train/test RMSE comparison
+  - `eventdisplay-ml-diagnostic-partial-dependence`: Validate model captures physics via partial dependence curves
+  - `eventdisplay-ml-diagnostic-residual-normality`: Validate residual normality and detect outliers
+- **Fixed sklearn FutureWarning**: Partial dependence plots convert feature data to float64 to avoid integer dtype warnings in newer scikit-learn versions