  Grid-based modelling is widely used for estimating stellar parameters.
However, stellar model grid is sparse because of the computational cost. This
paper demonstrates an application of a machine-learning algorithm using the
Gaussian Process (GP) Regression that turns a sparse model grid onto a
continuous function. We train GP models to map five fundamental inputs (mass,
equivalent evolutionary phase, initial metallicity, initial helium fraction,
and the mixing-length parameter) to observable outputs (effective temperature,
surface gravity, radius, surface metallicity, and stellar age). We test the GP
predictions for the five outputs using off-grid stellar models and find no
obvious systematic offsets, indicating good accuracy in predictions.As a
further validation, we apply these GP models to characterise 1,000 fake stars.
Inferred masses and ages determined with GP models well recover true values
within one standard deviation. An important consequence of using GP-based
interpolation is that stellar ages are more precise than those estimated with
the original sparse grid because of the full sampling of fundamental inputs.
