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Enhanced SHAP Analysis for Binary Classification Models

Source: R/eSHAP_plot.R
eSHAP_plot.Rd

The SHAP plot for classification models is a visualization tool that uses the Shapley value, an approach from cooperative game theory, to compute feature contributions for single predictions. The Shapley value fairly distributes the difference of the instance’s prediction and the datasets average prediction among the features. This method is available from the iml package.

Usage

eSHAP_plot(
  task,
  trained_model,
  splits,
  sample.size = 30,
  seed = 246,
  subset = 1
)

Arguments

task

mlr3 task object for binary classification

trained_model

mlr3 trained learner object

splits

mlr3 object defining data splits for train and test sets

sample.size

numeric, default to 30. The larger the value, the slower but more accurate the estimate of SHAP values

seed

numeric, an integer for reproducibility. Default to 246

subset

numeric, what percentage of the instances to use from 0 to 1 where 1 means all

Value

A list containing:

shap_plot

An enhanced SHAP plot with user interactive elements.

shap_Mean_wide

A matrix of SHAP values.

shap_Mean

A data.table with aggregated SHAP values.

shap

Raw SHAP values.

shap_pred_plot

A plot depicting SHAP values versus predicted probabilities.

References

Zargari Marandi, R., 2024. ExplaineR: an R package to explain machine learning models. Bioinformatics advances, 4(1), p.vbae049. Molnar C, Casalicchio G, Bischl B. iml: An R package for interpretable machine learning. Journal of Open Source Software. 2018 Jun 27;3(26):786.

See also

eSHAP_plot_reg()

Other classification: eSHAP_plot_multiclass()

Other SHAP: eSHAP_plot_multiclass()

Examples

# \donttest{
library("explainer")
seed <- 246
set.seed(seed)
# Load necessary packages
if (!requireNamespace("mlbench", quietly = TRUE)) stop("mlbench not installed.")
if (!requireNamespace("mlr3learners", quietly = TRUE)) stop("mlr3learners not installed.")
if (!requireNamespace("ranger", quietly = TRUE)) stop("ranger not installed.")
# Load BreastCancer dataset
utils::data("BreastCancer", package = "mlbench")
target_col <- "Class"
positive_class <- "malignant"
mydata <- BreastCancer[, -1]
mydata <- na.omit(mydata)
sex <- sample(c("Male", "Female"), size = nrow(mydata), replace = TRUE)
mydata$age <- as.numeric(sample(seq(18, 60), size = nrow(mydata), replace = TRUE))
mydata$sex <- factor(sex, levels = c("Male", "Female"), labels = c(1, 0))
maintask <- mlr3::TaskClassif$new(
  id = "my_classification_task",
  backend = mydata,
  target = target_col,
  positive = positive_class
)
splits <- mlr3::partition(maintask)
mylrn <- mlr3::lrn("classif.ranger", predict_type = "prob")
mylrn$train(maintask, splits$train)
SHAP_output <- eSHAP_plot(
  task = maintask,
  trained_model = mylrn,
  splits = splits,
  sample.size = 2, # also 30 or more
  seed = seed,
  subset = 0.02 # up to 1
)
#> Warning: Ignoring unknown aesthetics: text
# }