Magnetotelluric (MT) data inversion seeks to recover resistivity models of the subsurface. Solving the inversion problem is a non-trivial task, as multiple plausible solutions can be recovered due to the nonlinearity of the problem. To reduce this nonlinearity, we propose a data-driven approach where a 1-D cumulative resistance model is estimated from MT data via a direct data transformation. We define the cumulative representation of layered models as the weighted sum of layer thickness divided by resistivity from surface to any depth level, which is the cumulative conductance. Its inverse, cumulative resistance, is directly related to the real part of the impedance computed from MT data. We train a neural network to transform the MT impedance into a resistance model. The corresponding 1-D resistivity model is obtained without a priori information. We validate our approach using synthetic and real data, opening the discussion for future developments of this new perspective.