  We derive an advanced surrogate model for predicting turbulent transport in
the edge of tokamaks driven by electron temperature gradient (ETG) modes. Our
derivation is based on a recently developed sensitivity-driven sparse grid
interpolation approach for uncertainty quantification and sensitivity analysis
at scale, which informs the set of parameters that define the surrogate model
as a scaling law. Our model reveals that ETG-driven electron heat flux is
influenced by the safety factor $q$, electron beta $\beta_e$, and normalized
electron Debye length $\lambda_D$, in addition to well established parameters
such as the electron temperature and density gradients. To assess the
trustworthiness of our model's predictions beyond training, we compute
prediction intervals using bootstrapping. The surrogate model's predictive
power is tested across a wide range of parameter values, including
within-distribution testing parameters (to verify our model) as well as
out-of-bounds and out-of-distribution testing (to validate the proposed model).
Overall, validation efforts show that our model competes well with, or even
outperforms, existing scaling laws in predicting ETG-driven transport.
