  We consider the application of a machine learning technique to growth and
expansion rate data in the context of teleparallel gravity (TG). We do this by
using a combined approach of Hubble data together with
redshift-space-distortion $f\sigma_8$ data which together are used to
reconstruct the TG Lagrangian via Gaussian processes (GP), where the Hubble
data mainly comes from cosmic chronometer and supernova type Ia data from the
Pantheon release. In this work, we consider two main GP covariance functions,
namely the squared-exponential and Cauchy kernels in order to show consistency
(to within 1$\sigma$ uncertainties). The core results of this work are the
numerical constructions of the TG Lagrangian from GP reconstructed Hubble and
growth data. We take different possible combinations of the datasets and
kernels to show any potential differences in this regard. We show that
nontrivial cosmology beyond $\Lambda$CDM falls within the uncertainties of the
reconstruction from growth data.
