Fitting is the modelling of data where parameters of the model are allowed to vary during a fitting process until the model and data agree better according to some cost function.
In summary the Mantid fitting provides
Here a more advanced aspect of Mantid fitting is presented. We will fit an asymmetric peak from a GEM data set with the Ikeda-Carpenter function on a LinearBackground.
Not a very good job. The Ikeda-Carpenter function is a better choice here. But this is a very difficult function to work with. It requires very good initial parameter values for the fit to converge. The Mantid approach is to use the pre-set values which are defined on a per-instrument basis. For example, when the Ikeda-Carpenter is used with GEM data the fitting tools automatically find and set the appropriate initial values. This results in a good fit.