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The fit method

A compulsory fit method returns three objects:

MMI.fit(model::SomeSupervisedModel, verbosity, X, y) -> fitresult, cache, report

  1. fitresult is the fitresult in the sense above (which becomes an argument for predict discussed below).

  2. report is a (possibly empty) NamedTuple, for example, report=(deviance=..., dof_residual=..., stderror=..., vcov=...). Any training-related statistics, such as internal estimates of the generalization error, and feature rankings, should be returned in the report tuple. How, or if, these are generated should be controlled by hyperparameters (the fields of model). Fitted parameters, such as the coefficients of a linear model, do not go in the report as they will be extractable from fitresult (and accessible to MLJ through the fitted_params method described below).

  3. The value of cache can be nothing, unless one is also defining an update method (see below). The Julia type of cache is not presently restricted.

Note

The fit (and update) methods should not mutate the model. If necessary, fit can create a deepcopy of model first.

It is not necessary for fit to provide type or dimension checks on X or y or to call clean! on the model; MLJ will carry out such checks.

The types of X and y are constrained by the input_scitype and target_scitype trait declarations; see Trait declarations below. (That is, unless a data front-end is implemented, in which case these traits refer instead to the arguments of the overloaded reformat method, and the types of X and y are determined by the output of reformat.)

The method fit should never alter hyperparameter values, the sole exception being fields of type <:AbstractRNG. If the package is able to suggest better hyperparameters, as a byproduct of training, return these in the report field.

The verbosity level (0 for silent) is for passing to the learning algorithm itself. A fit method wrapping such an algorithm should generally avoid doing any of its own logging.

Sample weight support. If supports_weights(::Type{<:SomeSupervisedModel}) has been declared true, then one instead implements the following variation on the above fit:

MMI.fit(model::SomeSupervisedModel, verbosity, X, y, w=nothing) -> fitresult, cache, report