additive_shap: Additive SHAP

In kernelshap: Kernel SHAP

Additive SHAP

Description

Exact additive SHAP assuming feature independence. The implementation works for models fitted via

• lm(),

• glm(),

• mgcv::gam(),

• mgcv::bam(),

• gam::gam(),

• survival::coxph(), and

• survival::survreg().

Usage

additive_shap(object, X, verbose = TRUE, ...)

Arguments

object	Fitted model object.
X	Dataframe with rows to be explained. Will be used like predict(object, newdata = X, type = "terms").
verbose	Set to FALSE to suppress messages and the progress bar.
...	Currently unused.

Details

The SHAP values are extracted via predict(object, newdata = X, type = "terms"), a logic heavily inspired by fastshap:::explain.lm(..., exact = TRUE). Models with interactions (specified via : or *), or with terms of multiple features like log(x1/x2) are not supported.

Note that the SHAP values obtained by additive_shap() are expected to match those of permshap() and kernelshap() as long as their background data equals the full training data (which is typically not feasible).

Value

An object of class "kernelshap" with the following components:

• S: (n \times p) matrix with SHAP values.

• X: Same as input argument X.

• baseline: The baseline.

• exact: TRUE.

• txt: Summary text.

• predictions: Vector with predictions of X on the scale of "terms".

• algorithm: "additive_shap".

Examples

# MODEL ONE: Linear regression
fit <- lm(Sepal.Length ~ ., data = iris)
s <- additive_shap(fit, head(iris))
s

# MODEL TWO: More complicated (but not very clever) formula
fit <- lm(
  Sepal.Length ~ poly(Sepal.Width, 2) + log(Petal.Length) + log(Sepal.Width),
  data = iris
)
s_add <- additive_shap(fit, head(iris))
s_add

# Equals kernelshap()/permshap() when background data is full training data
s_kernel <- kernelshap(
 fit, head(iris[c("Sepal.Width", "Petal.Length")]), bg_X = iris
)
all.equal(s_add$S, s_kernel$S)

kernelshap documentation built on Sept. 11, 2024, 9:35 p.m.