  Employing the neural network framework, we obtain empirical fits to the
electron-scattering cross sections for carbon over a broad kinematic region,
extending from the quasielastic peak through resonance excitation to the onset
of deep-inelastic scattering. We consider two different methods of obtaining
such model-independent parametrizations and the corresponding uncertainties:
based on the bootstrap approach and the Monte Carlo dropout approach. In our
analysis, the $\chi^2$ defines the loss function, including point-to-point and
normalization uncertainties for each independent set of measurements. Our
statistical approaches lead to fits of comparable quality and similar
uncertainties of the order of $7$%. To test these models, we compare their
predictions to test datasets excluded from the training process and theoretical
predictions obtained within the spectral function approach. The predictions of
both models agree with experimental measurements and theoretical calculations.
We also perform a comparison to a dataset lying beyond the covered kinematic
region, and find that the bootstrap approach shows better interpolation and
extrapolation abilities than the one based on the dropout algorithm.
