  The non-spherical shapes of dark matter and gas distributions introduce
systematic uncertainties that affect observable-mass relations and selection
functions of galaxy groups and clusters. However, the triaxial gas
distributions depend on the non-linear physical processes of halo formation
histories and baryonic physics, which are challenging to model accurately. In
this study we explore a machine learning approach for modelling the dependence
of gas shapes on dark matter and baryonic properties. With data from the
IllustrisTNG hydrodynamical cosmological simulations, we develop a machine
learning pipeline that applies \pkg{XGBoost}, an implementation of gradient
boosted decision trees, to predict radial profiles of gas shapes from halo
properties. We show that \pkg{XGBoost} models can accurately predict gas shape
profiles in dark matter haloes. We also explore model interpretability with
\pkg{SHAP}, a method that identifies the most predictive properties at
different halo radii. We find that baryonic properties best predict gas shapes
in halo cores, whereas dark matter shapes are the main predictors in the halo
outskirts. This work demonstrates the power of interpretable machine learning
in modelling observable properties of dark matter haloes in the era of
multi-wavelength cosmological surveys.
