fastshap (version 0.0.7)

force_plot: Additive force plots

Description

Visualize Shapley values with additive force style layouts from the Python shap package.

Usage

force_plot(object, ...)
# S3 method for explain
force_plot(
  object,
  baseline = NULL,
  feature_values = NULL,
  display = c("viewer", "html"),
  ...
)

Arguments

object

An object of class "explain".

...

Additional optional arguments. (Currently ignored.)

baseline

Numeric giving the average prediction across all the training observations. NOTE: It is recommended to provide this argument whenever object contains approximate Shapley values.

feature_values

A matrix-like R object (e.g., a data frame or matrix) containing the corresponding feature values for the explanations in object.

display

Character string specifying how to display the results. Current options are "viewer" (default) and "html". The latter is necessary for viewing the display inside of an rmarkdown document.

Details

The resulting plot shows how each feature contributes to push the model output from the baseline prediction (i.e., the average predicted outcome over the entire training set `X`) to the corresponding model output. Features pushing the prediction higher are shown in red, while those pushing the prediction lower are shown in blue.

References

Lundberg, Scott M, Bala Nair, Monica S Vavilala, Mayumi Horibe, Michael J Eisses, Trevor Adams, David E Liston, et al. 2018. "Explainable Machine-Learning Predictions for the Prevention of Hypoxaemia During Surgery." Nature Biomedical Engineering 2 (10). Nature Publishing Group: 749.

Examples

if (FALSE) {
#
# A projection pursuit regression (PPR) example
#

# Load the sample data; see ?datasets::mtcars for details
data(mtcars)

# Fit a projection pursuit regression model
mtcars.ppr <- ppr(mpg ~ ., data = mtcars, nterms = 1)

# Compute approximate Shapley values using 10 Monte Carlo simulations
set.seed(101)  # for reproducibility
shap <- explain(mtcars.ppr, X = subset(mtcars, select = -mpg), nsim = 10, 
                pred_wrapper = predict, adjust = TRUE)

# Visualize first explanation
preds <- predict(mtcars.ppr, newdata = mtcars)
x <- subset(mtcars, select = -mpg)[1L, ]  # take first row of feature values
force_plot(shap[1L, ], baseline = mean(preds), feature_values = x)
}