  Estimating stellar masses and radii is a challenge for most of the stars but
their knowledge is critical for many different astrophysical fields. One of the
most extended techniques for estimating these variables are the so-called
empirical relations. In this work we propose a group of state-of-the-art AI
regression models, with the aim of studying their proficiency in estimating
stellar masses and radii. We publicly release the database, the AI models, and
an online tool for stellar mass and radius estimation to the community. We use
a sample of 726 MS stars in the literature with accurate M, R, T_eff, L, log g,
and [Fe/H]. We have split our data sample into training and testing sets and
then analyzed the different AI techniques with them. In particular, we have
experimentally evaluated the accuracy of the following models: Linear Reg.,
Bayesian Reg., Regression Trees, Random Forest, Support-Vector Reg. (SVR),
Neural Networks, kNN, and Stacking. We propose a series of experiments designed
to evaluate the accuracy of the estimations. We have also analyzed the impact
of reducing the number of inputs parameters and compared our results with those
from state-of-the-art empirical relations in the literature. We have found that
a Stacking of several regression models is the most suitable technique for
estimating masses and radii. In the case of the mass, Neural Networks also
provide precise results, and for the radius, SVR and Neural Networks work too.
When comparing with other state-of-the-art empirical relations based models,
our Stacking improves the accuracy by a factor of two for both variables. In
addition, bias is reduced to one order of magnitude in the case of the stellar
mass. Finally, we have found that using our Stacking and only T_eff and L as
input features, the accuracies obtained are slightly larger than a 5%, with a
bias approx 1.5%.
