In order to fit DFT-NEB barriers using the LCE model, we have implemented the Fitting class in kmcpy.fitting. This function performs fitting by interfacing with the python package scikit-learn . kMCpy also stores fitted results (i.e., the KECIs) in a portable JSON format. The current implementation of kMCpy uses the "LASSO" regression to perform fitting. Indeed, LASSO limits the selection of orbits in the fit to the most important ones. LASSO requires a userspecified α parameter to reduce the total number of selected orbits. The Fitting class stores the fitting history, e.g. α and weights used during LASSO regression, for keeping a record and to fine tune the LCE.