Eventdisplay-ML - a toolkit to interface and run machine learning methods together with the Eventdisplay software package for gamma-ray astronomy data analysis. Stereo (direction and energy) reconstruction tested and validated on both VERITAS and CTAO simulations plus VERITAS data. Compatible with: Eventdisplay for CTAO simulations v5.17.0 Eventdisplay for VERITAS v4.92 (not released yet) Changelog New Features Add --max_tel_per_type 10 argument to restrict the number of telescope parameters per telescope type. Fix bug in indexing arrays with non-continuous telescope identifiers. (#49) Improve stereo reconstruction by adding the geometrical feature img2_ang. Change clipping min for size to '1' (applicable for small images in SSTs). Add preview_rows as command line parameter to allow flexible printout for debugging. (#51) 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) 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 (#53) Maintenance Update g/h separation to new sorting scheme of telescope-dependent variables. (#45) Add early stopping to classification. Increase number of estimators. (#48) Add detailed copilot instructions. (#50) Bugfixes 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

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