Description

I have recently made a multi-class model and have been examining the SHAP.
Typically SHAP values are in the range of around [-10,10], but my model is outputting SHAP values in the range of +/- thousands...
This is replicated using the lgbm_learner.predict(X_test, pred_contrib=True) and using the SHAP packages TreeExplainer.

I have narrowed down a way to mitigate this issues, by adjusting the LightGBM parameters.
Specifically, either:

• increasing min_data_in_leaf (to above 58)
• increasing min_sum_hessian_in_leaf (to above 1e-2)

It is worth noting, I haven't found any issues with binary prediction, just multi-class.

Reproducible example

I have tried very hard to produce an example of this with synthetic data, but I have not been able to recreate the problem (and unfortunately, I cannot share the data used).
This makes me this there is something quirky going on with my data and LightGBM.
My features are either float64 or int64, and I have tried both an int and catagorical type target.

Environment info

Python Version: python 3.11.10
LightGBM version: lightgbm 4.5.0

Command(s) you used to install LightGBM

conda install lightgbm

Additional Comments

Even though I cannot reproduce this problem with synthetic data, I thought it would be good to report it (I hope this is ok).