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ExplaineR: an R package to explain machine learning models.

Ramtin Zargari Marandi1

  • 1Centre of Excellence for Health, Immunity and Infections (CHIP), Rigshospitalet, Copenhagen University Hospital, DK-2100 Copenhagen, Denmark.

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|April 5, 2024
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Summary
This summary is machine-generated.

ExplaineR is a new R package that enhances SHapley Additive exPlanations (SHAP) analysis for machine learning models. It offers clustering and interactive visualizations for deeper model interpretation and reporting.

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Area of Science:

  • Machine Learning
  • Data Science
  • Computational Statistics

Background:

  • SHapley Additive exPlanations (SHAP) is a crucial method for interpreting machine learning models.
  • Existing tools often limit the full utilization of SHAP's potential for in-depth analysis.
  • There is a need for specialized software to enhance SHAP-based model interpretation.

Purpose of the Study:

  • Introduce ExplaineR, an R package designed to facilitate the interpretation of binary classification and regression models using SHAP.
  • Provide advanced functionalities for SHAP analysis, including clustering and interactive visualizations.
  • Enable comprehensive reporting of machine learning model performance and interpretation.

Main Methods:

  • Development of the ExplaineR R package, incorporating clustering for SHAP analysis.
  • Implementation of user-interactive visualizations for model evaluation, fairness, and decision-curve analysis.
  • Integration of diverse SHAP plotting capabilities for detailed pattern identification.

Main Results:

  • ExplaineR enables pinpointing significant patterns in SHAP plots and tracing them back to specific instances via SHAP clustering.
  • The package supports the identification of patient subgroups within clinical cohorts, acting as a robust profiling tool.
  • Users can generate comprehensive reports on machine learning outcomes, ensuring consistent documentation.

Conclusions:

  • ExplaineR significantly enhances the utility of SHAP for post-prediction analysis in machine learning.
  • The package empowers users with advanced tools for model interpretation, fairness assessment, and subgroup discovery.
  • ExplaineR facilitates thorough and reproducible documentation of machine learning model performance and insights.