A Julia package for interpretable machine learning with stochastic Shapley values
72 Stars
Updated Last
12 Months Ago
Started In
January 2020

Build Status Codecov

ShapML ShapML logo

The purpose of ShapML is to compute stochastic feature-level Shapley values which can be used to (a) interpret and/or (b) assess the fairness of any machine learning model. Shapley values are an intuitive and theoretically sound model-agnostic diagnostic tool to understand both global feature importance across all instances in a data set and instance/row-level local feature importance in black-box machine learning models.

This package implements the algorithm described in Štrumbelj and Kononenko's (2014) sampling-based Shapley approximation algorithm to compute the stochastic Shapley values for a given instance and model feature.

  • Flexibility:

    • Shapley values can be estimated for any machine learning model using a simple user-defined predict() wrapper function.
  • Speed:

    • The speed advantage of ShapML comes in the form of giving the user the ability to select 1 or more target features of interest and avoid having to compute Shapley values for all model features (i.e., a subset of target features from a trained model will return the same feature-level Shapley values as the full model with all features). This is especially useful in high-dimensional models as the computation of a Shapley value is exponential in the number of features.


using Pkg
  • Development
using Pkg
Pkg.add(PackageSpec(url = ""))

Documentation and Vignettes