Eventdisplay-ML: a machine learning toolkit for Eventdisplay

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 v5.18.1 Eventdisplay for VERITAS v4.92 (not released yet) New Features Add gamma/hadron classification training options to balance signal/background zenith-bin distributions and to omit ze_bin as a stress-test feature. Add zenith-uniformity summary metrics and multi-training comparison plots for gamma/hadron performance diagnostics. (#72) Introduce --model_prefix_high_multiplicity parameter for stereo analysis to use different models for 2-tel and >2-tel multiplicity events. (#73) Change training weights for stereo reconstruction, to avoid extreme weights leading to early training abortion. (#74) Maintenance Reduce peak memory usage during chunked data loading and regression training. (#70) Address numpy deprecation warnings. (#71)