explanation_traits: Explanation Methods per Algorithm
Description
What explain does to a model fitted by each supervised algorithm: which
estimator setup_SHAP(estimator = "auto") resolves to, whether that answer
is exact, which value function it computes, and why.
Usage
explanation_traits(algorithm = NULL)
Value
data.frame: One row per algorithm.
Arguments
algorithm
Optional Character: Name of a supervised algorithm, matched
case-insensitively. NULL returns every algorithm.
Author
EDG
Details
The columns:
name
The algorithm, as named by setup_<name>().
estimator
The estimator "auto" resolves to. "KernelSHAP" is
the model-agnostic fallback and applies to every algorithm; the others
are exact and algorithm-specific.
exact
TRUE if the estimator returns Shapley values rather than an
estimate of them, NA where that depends on the fitted model rather than
on the algorithm -- an ensemble whose meta-learner turned out to be
linear, or a model that has one linear map for a regression or binary
outcome but not for a multiclass one. An NA row explains what it did
when asked, and refuses with a message naming the reason when it cannot.
perturbation
The value function computed when
setup_SHAP(perturbation = ) is left NULL.
rationale
Why this estimator, in one line.
estimator = "kernel" is always available, so a row whose exact is FALSE
is saying that estimator = "exact" has nothing to offer for it -- not that
the algorithm cannot be explained.
explanation_traits()
explanation_traits("Ranger")
# Which algorithms have an exact estimator?em <- explanation_traits()
em[["name"]][!is.na(em[["exact"]]) & em[["exact"]]]