SHAP importance: how each variable pushes each prediction, up or down.

Shapley values (Lundberg & Lee 2017) of the network's predictions, estimated from its gradients. Each point's prediction, less a reference prediction, is shared among the variables, with a sign: this one raised it, that one lowered it. Averaged in absolute value over the points they are a global importance – of how much each variable MOVES the predictions, which is not how much it improves them (permutation_importance() measures that); kept point by point ($points) they are the map of what drives the prediction where (Padarian et al. 2020; Wadoux et al. 2023 for soil carbon).

Usage

shap_importance(
  estimator = c("expected_gradients", "integrated_gradients", "kernel"),
  samples = 100L,
  background = NULL,
  steps = 50L,
  permutations = 64L,
  max_points = NULL,
  seed = 42L
)

Arguments

estimator: "expected_gradients" (the default; Erion et al. 2021): the reference is a background of the model's own training points, drawn at random, so no baseline is chosen by hand – the values are SHAP values of the network, and the same estimator as SHAP's GradientExplainer. "integrated_gradients" (Sundararajan et al. 2017): one baseline, the training mean of every channel – the average landscape, flat, which no point is – kept because its sum is exact rather than sampled. "kernel": the Shapley values of the variables themselves, computed from coalitions, with no gradient (Lundberg & Lee 2017's KernelSHAP game): the value of a set of variables is the mean prediction, over a background of training points, with the variables outside it taken from the background point – the whole patch, the same point in every window. Exact over every coalition up to 14 variables; by sampled permutations above, up to 40. Meant for a few variables – give groups themes – and a few points. Read against the gradient estimators: they share the total and split it alike when the variables do not interact, so where they part is how much the variables do.
samples: For expected gradients: draws per point, each a background point and a place on the straight path to it. The noise they leave is reported.
background: How many of the model's training points the references are drawn from. NULL: 200 for expected gradients, 16 for the kernel – each coalition costs one pass per background point.
steps: For integrated gradients: points on the path.
permutations: For the kernel above 14 variables: permutations sampled (in pairs, each with its reverse).
max_points: Explain this many of the rows only, drawn at random – the same rows for every estimator, so two can be compared point by point. NULL: every row for the gradient estimators, 200 for the kernel.
seed: Seed of the draws, shared by every model.

Value

An importance_spec, for dsm_importance().

Details

The values are in the units the network predicts in: for a log1p target, a SHAP value of +0.1 raises the predicted value by about 10%. Each point's values add up to its prediction less the background's mean prediction (expected gradients) or less the prediction at the baseline (integrated gradients); dsm_importance() checks that they do and stops if they do not. SHAP by deep-network rules (DeepSHAP) is not offered: it needs a propagation rule written for every layer of this architecture.

Examples

shap_importance()
#> <importance_spec> SHAP by expected gradients, 100 sample(s) over 200 background point(s)
shap_importance("kernel", background = 8, max_points = 50)
#> <importance_spec> SHAP by kernel, Shapley over the variables, 8 background point(s), at most 50 point(s)