kernelshap: Kernel SHAP

In kernelshap: Kernel SHAP

Kernel SHAP

Description

Efficient implementation of Kernel SHAP, see Lundberg and Lee (2017), and Covert and Lee (2021), abbreviated by CL21. By default, for up to p=8 features, exact SHAP values are returned (with respect to the selected background data). Otherwise, a partly exact hybrid algorithm combining exact calculations and iterative paired sampling is used, see Details.

Usage

kernelshap(object, ...)

## Default S3 method:
kernelshap(
  object,
  X,
  bg_X = NULL,
  pred_fun = stats::predict,
  feature_names = colnames(X),
  bg_w = NULL,
  bg_n = 200L,
  exact = length(feature_names) <= 8L,
  hybrid_degree = 1L + length(feature_names) %in% 4:16,
  m = 2L * length(feature_names) * (1L + 3L * (hybrid_degree == 0L)),
  tol = 0.005,
  max_iter = 100L,
  parallel = FALSE,
  parallel_args = NULL,
  verbose = TRUE,
  seed = NULL,
  ...
)

## S3 method for class 'ranger'
kernelshap(
  object,
  X,
  bg_X = NULL,
  pred_fun = NULL,
  feature_names = colnames(X),
  bg_w = NULL,
  bg_n = 200L,
  exact = length(feature_names) <= 8L,
  hybrid_degree = 1L + length(feature_names) %in% 4:16,
  m = 2L * length(feature_names) * (1L + 3L * (hybrid_degree == 0L)),
  tol = 0.005,
  max_iter = 100L,
  parallel = FALSE,
  parallel_args = NULL,
  verbose = TRUE,
  seed = NULL,
  survival = c("chf", "prob"),
  ...
)

Arguments

object	Fitted model object.
...	Additional arguments passed to pred_fun(object, X, ...).
X	(n \times p) matrix or data.frame with rows to be explained. The columns should only represent model features, not the response (but see feature_names on how to overrule this).
bg_X	Background data used to integrate out "switched off" features, often a subset of the training data (typically 50 to 500 rows). In cases with a natural "off" value (like MNIST digits), this can also be a single row with all values set to the off value. If no bg_X is passed (the default) and if X is sufficiently large, a random sample of bg_n rows from X serves as background data.
pred_fun	Prediction function of the form ⁠function(object, X, ...)⁠, providing K \ge 1 predictions per row. Its first argument represents the model object, its second argument a data structure like X. Additional (named) arguments are passed via .... The default, stats::predict(), will work in most cases.
feature_names	Optional vector of column names in X used to calculate SHAP values. By default, this equals colnames(X).
bg_w	Optional vector of case weights for each row of bg_X. If bg_X = NULL, must be of same length as X. Set to NULL for no weights.
bg_n	If bg_X = NULL: Size of background data to be sampled from X.
exact	If TRUE, the algorithm will produce exact SHAP values with respect to the background data. The default is TRUE for up to eight features, and FALSE otherwise.
hybrid_degree	Integer controlling the exactness of the hybrid strategy. For 4 \le p \le 16, the default is 2, otherwise it is 1. Ignored if exact = TRUE. 0: Pure sampling strategy not involving any exact part. It is strictly worse than the hybrid strategy and should therefore only be used for studying properties of the Kernel SHAP algorithm. 1: Uses all 2p on-off vectors z with \sum z \in \{1, p-1\} for the exact part. The remaining mass is covered by random sampling. 2: Uses all p(p+1) on-off vectors z with \sum z \in \{1, 2, p-2, p-1\}. The remaining mass is covered by sampling. Usually converges fast. k>2: Uses all on-off vectors with \sum z \in \{1, \dots, k, p-k, \dots, p-1\}.
m	Even number of on-off vectors sampled during one iteration. The default is 2p, except when hybrid_degree == 0. Then it is set to 8p. Ignored if exact = TRUE.
tol	Tolerance determining when to stop. As in CL21, the algorithm keeps iterating until \textrm{max}(\sigma_n)/(\textrm{max}(\beta_n) - \textrm{min}(\beta_n)) < \textrm{tol}, where the \beta_n are the SHAP values of a given observation, and \sigma_n their standard errors. For multidimensional predictions, the criterion must be satisfied for each dimension separately. The stopping criterion uses the fact that standard errors and SHAP values are all on the same scale. Ignored if exact = TRUE. For permshap(), the default is 0.01, while for kernelshap() it is set to 0.005.
max_iter	If the stopping criterion (see tol) is not reached after max_iter iterations, the algorithm stops. Ignored if exact = TRUE.
parallel	If TRUE, use foreach::foreach() and ⁠%dofuture%⁠ to loop over rows to be explained. Must register backend beforehand, e.g., plan(multisession), see README for an example. Currently disables the progress bar.
parallel_args	Named list of arguments passed to foreach::foreach(.options.future = ...), ideally NULL (default). Only relevant if parallel = TRUE. Example on Windows: if object is a GAM fitted with package 'mgcv', then one might need to set parallel_args = list(packages = "mgcv"). Similarly, if the model has been fitted with ranger(), then it might be necessary to pass parallel_args = list(packages = "ranger").
verbose	Set to FALSE to suppress messages and the progress bar.
seed	Optional integer random seed. Note that it changes the global seed.
survival	Should cumulative hazards ("chf", default) or survival probabilities ("prob") per time be predicted? Only in ranger() survival models.

Details

The pure iterative Kernel SHAP sampling as in Covert and Lee (2021) works like this:

1. A binary "on-off" vector z is drawn from \{0, 1\}^p according to a special weighting logic.

2. For each j with z_j = 1, the j-th column of the original background data is replaced by the corresponding feature value x_j of the observation to be explained.

3. The average prediction v_z on the data of Step 2 is calculated, and the average prediction v_0 on the background data is subtracted.

4. Steps 1 to 3 are repeated m times. This produces a binary m \times p matrix Z (each row equals one of the z) and a vector v of shifted predictions.

5. v is regressed onto Z under the constraint that the sum of the coefficients equals v_1 - v_0, where v_1 is the prediction of the observation to be explained. The resulting coefficients are the Kernel SHAP values.

This is repeated multiple times until convergence, see CL21 for details.

To avoid the re-evaluation of identical coalition vectors, we have implemented a hybrid strategy, combining exact calculations with sampling.

The hybrid algorithm has two steps:

1. Step 1 (exact part): There are 2p different on-off vectors z with \sum z \in \{1, p-1\}. The degree 1 hybrid will list those vectors and use them according to their weights in the upcoming calculations. Depending on p, we can also go a step further to a degree 2 hybrid by adding all p(p-1) vectors with \sum z \in \{2, p-2\} to the process etc. The necessary predictions are obtained along with other calculations similar to those described in CL21.

2. Step 2 (sampling part): The remaining weight is filled by sampling vectors z according to Kernel SHAP weights normalized to the values not yet covered by Step 1. Together with the results from Step 1 - correctly weighted - this now forms a complete iteration as in CL21. The difference is that a significant part of the mass is covered by exact calculations. Afterwards, the algorithm iterates until convergence. The output of Step 1 is reused in every iteration.

If p is sufficiently small, all possible 2^p-2 on-off vectors z can be evaluated. In this case, no sampling is required and the algorithm returns exact Kernel SHAP values with respect to the given background data. Since kernelshap() calculates predictions on data with MN rows (N is the background data size and M the number of z vectors), p should not be higher than 10 for exact calculations. For similar reasons, degree 2 hybrids should not use p larger than 40.

Value

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

• S: (n \times p) matrix with SHAP values or, if the model output has dimension K > 1, a list of K such matrices.

• X: Same as input argument X.

• baseline: Vector of length K representing the average prediction on the background data.

• bg_X: The background data.

• bg_w: The background case weights.

• m_exact: Number of on-off vectors evaluated for exact calculations.

• prop_exact: Proportion of the Kernel SHAP weight distribution covered by exact calculations.

• exact: Logical flag indicating whether calculations are exact or not.

• txt: Summary text.

• predictions: (n \times K) matrix with predictions of X.

• algorithm: "kernelshap".

• m: Number of sampled on-off vectors evaluated per iteration (if not exact).

• SE: Standard errors corresponding to S (if not exact).

• n_iter: Integer vector of length n providing the number of iterations per row of X (if not exact).

• converged: Logical vector of length n indicating convergence per row of X (if not exact).

Methods (by class)

• kernelshap(default): Default Kernel SHAP method.

• kernelshap(ranger): Kernel SHAP method for "ranger" models, see Readme for an example.

References

1. Scott M. Lundberg and Su-In Lee. A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017.

2. Ian Covert and Su-In Lee. Improving KernelSHAP: Practical Shapley Value Estimation Using Linear Regression. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:3457-3465, 2021.

Examples

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

# Select rows to explain (only feature columns)
X_explain <- iris[-1]

# Calculate SHAP values
s <- kernelshap(fit, X_explain)
s

# MODEL TWO: Multi-response linear regression
fit <- lm(as.matrix(iris[, 1:2]) ~ Petal.Length + Petal.Width + Species, data = iris)
s <- kernelshap(fit, iris[3:5])
s

# Note 1: Feature columns can also be selected 'feature_names'
# Note 2: Especially when X is small, pass a sufficiently large background data bg_X
s <- kernelshap(
  fit,
  iris[1:4, ],
  bg_X = iris,
  feature_names = c("Petal.Length", "Petal.Width", "Species")
)
s

kernelshap documentation built on Nov. 5, 2025, 6:16 p.m.