ShapML.jl

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A Julia package for interpretable machine learning with stochastic Shapley values

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최종 버전 다운로드 (.zip)

- - ci.yml
  - CompatHelper.yml
  - TagBot.yml
- - featureeffects.png
  - featureimportance.png
  - index.md
  - shapoutput.PNG
- make.jl
- Project.toml
- aggregate.jl
- predict.jl
- shap_sample.jl
- ShapML.jl
- runtests.jl
- shap.jl
- feature_effects.png
- feature_importance_example.png
- shap_output.PNG
- ShapML_logo.png
.gitignore
LICENSE
Project.toml
README.md

// repository documentation

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# ShapML <img src="./tools/ShapML_logo.png" alt="ShapML logo" align="right" height="138.5" style="display: inline-block;">

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](https://christophm.github.io/interpretable-ml-book/shapley.html)**
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](https://link.springer.com/article/10.1007%2Fs10115-013-0679-x)
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.

## Install

* **[pkg.julialang.org](https://pkg.julialang.org)**

``` julia
using Pkg
Pkg.add("ShapML")
```

* Development

``` julia
using Pkg
Pkg.add(PackageSpec(url = "https://github.com/nredell/ShapML.jl"))
```

## Documentation and Vignettes

* **[Docs](https://nredell.github.io/ShapML.jl/dev/)** (incuding examples from [MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/))

* **[Consistency with TreeSHAP](https://nredell.github.io/ShapML.jl/dev/vignettes/consistency/)**

* **[Speed - Julia vs Python vs R](https://nredell.github.io/docs/julia_speed)**

ShapML.jl

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