up

Author: jeremiedb

NEWS.md

@@ -6,10 +6,11 @@
 - Computations are now alsmost entirely done through `KernelAbstractions.jl`. Objective is to eventually have full support for AMD / ROCm in addition to current NVIDIA / CUDA devices.
 - Important performance increase, notably for larger max depth. Training time is now closely increase linearly with depth. 
 
-### Improved reproducibility
+### Breaking change: improved reproducibility
 - Training returns exactly the same fitted  model for a given learner (ex: `EvoTreeRegressor`). 
 - Reproducibility is respected for both `cpu` and `gpu`. However, thes result may differ between `cpu` and `gpu`. Ie: reproducibility is guaranteed only within the same device type.
 - The learner / model constructor (ex: `EvoTreeRegressor`) now has a `seed::Int` argument to set the random seed. Legacy `rng` kwarg will now be ignored.
+- The internal random generator is now `Xishiro` (was previously `MersenneTwister` with `rng::Int`).
 
 ### Added node weight information in fitted trees 
 - The train weight reaching each of the split/leaf nodes is now stored in the fitted trees. This is accessible via `model.trees[i].w` for the i-th tree in the fitted model. This is notably inteded to support SHAP value computations.