get_risk: Get cross-validated risk of entire procedure (i.e.,...
In benkeser/cvma: Cross-validation-based maximal association

Get cross-validated risk of entire procedure (i.e., outer-most cross-validation layer)

1 2	get_risk(Y, V, all_fit_tasks, all_fits, all_weight, folds, all_sl, sl_control, y_weight_control, learners)

`Y`	A matrix or data.frame of outcomes
`V`	Number of outer folds of cross-validation (nested cross-validation uses V-1 and V-2 folds), so must be at least four.
`all_fit_tasks`	A list of all learner fitting tasks (quicker to search over than `all_fits`).
`all_fits`	A list of all learner fits (from `get_fit`)
`all_weight`	A list of all outcome weights (from `get_y_weight`)
`folds`	Vector identifying which fold observations fall into.
`all_sl`	A list of all super learner fits (from `get_sl`)
`sl_control`	A list with named entries ensemble.fn, optim_risk_fn, weight_fn, cv_risk_fn, family. Available functions can be viewed with `sl_control_options()`. See `?sl_control_options` for more on how users may supply their own functions.
`y_weight_control`	A list with named entries ensemble.fn, optim_risk_fn, weight_fn, cv_risk_fn. Available functions can be viewed with `y_weight_control_options()`. See `?y_weight_control_options` for more on how users may supply their own functions.
`learners`	Super learner wrappers. See `SuperLearner::listWrappers`.