Lists the supported engines, the package each needs, whether that package is installed, and how readable the fitted model is.
Value
A tibble::tibble() with columns engine, package, class_box
and installed.
Details
class_box grades readability by where an explanation comes from:
"glass" when the fitted model can simply be read, "grey" when its own
structure yields exact per-feature attributions, "black" when attributions
come only from perturbing the inputs, and "closed" when the model learns
its own representation first, so a per-input attribution no longer describes
what the model reasons in. Gradient boosting sits in the middle rather than
with the black boxes, which is where a two-way split would put it.
Examples
sdt_engines()
#> # A tibble: 9 × 4
#> engine package class_box installed
#> <chr> <chr> <chr> <lgl>
#> 1 tree rpart glass TRUE
#> 2 multinom nnet glass TRUE
#> 3 glmnet glmnet glass TRUE
#> 4 mars earth grey TRUE
#> 5 xgboost xgboost grey TRUE
#> 6 ranger ranger black TRUE
#> 7 svm_rbf kernlab black TRUE
#> 8 mlp nnet black TRUE
#> 9 transformer keras3 closed TRUE