eSHAP_plot_multiclass: Enhanced SHAP Analysis for Multi-Class Classification Models

In explainer: Machine Learning Model Explainer

Enhanced SHAP Analysis for Multi-Class Classification Models

Description

The SHAP plot for multi-class classification models is a visualization tool that uses the Shapley value to compute feature contributions for single predictions across multiple classes.

Usage

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

Arguments

task	mlr3 task object for multi-class 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:

combined_plots	SHAP plot depicting the SHAP values for each class
shap_data	A matrix of SHAP values for each class.
combined_all_classes	overall SHAP plot depicting the SHAP values for all classes on a single plot

See Also

Other classification: eSHAP_plot()

Other SHAP: eSHAP_plot()

Examples

library("explainer")
seed <- 246
set.seed(seed)
# Load necessary packages and data...

explainer documentation built on Oct. 1, 2024, 1:07 a.m.