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Techniques to improve ecological interpretability of black-box machine learning models.

Thomas Welchowski1, Kelly O Maloney2, Richard Mitchell3

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Gradient boosted trees (GBT) offer powerful ecological data modeling but are often black boxes. New interpretation tools like partial dependence plots (PDP), individual conditional expectation (ICE), and accumulated local effects (ALE) help reveal complex relationships.

Keywords:
boostinginteraction termsinterpretable machine learningmacroinvertebratesstream health

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Area of Science:

  • Environmental Science
  • Ecological Modeling
  • Statistical Ecology

Background:

  • Ecological data often involves numerous variables, nonlinear relationships, and interactions.
  • Gradient boosted trees (GBT) excel at modeling complex ecological data but are often considered 'black boxes'.
  • Interpreting GBT models is crucial for advancing environmental research and understanding ecological processes.

Purpose of the Study:

  • To introduce and evaluate recently developed statistical tools for interpreting black-box GBT models in environmental science.
  • To assess the utility of partial dependence plots (PDP), individual conditional expectation (ICE) curves, and accumulated local effects (ALE) for analyzing covariate-response relationships.
  • To quantify interaction effects using interaction strength (IAS) and Friedman's H² statistic.

Main Methods:

  • Application of Gradient Boosted Trees (GBT) to model stream biological health using a benthic macroinvertebrate biotic index in the contiguous U.S.
  • Utilized partial dependence plots (PDP), individual conditional expectation (ICE) curves, and accumulated local effects (ALE) for model interpretation.
  • Quantified interaction effects using interaction strength (IAS) and Friedman's H² statistic.

Main Results:

  • GBT models effectively identified key variables influencing stream health, including ecoregion, bed stability, watershed area, riparian vegetation, and catchment slope.
  • PDP, ICE, and ALE plots demonstrated advantages and limitations in identifying covariate-response relationships.
  • Identified interaction effects frequently involved the most important predictor variables.

Conclusions:

  • Interpretable machine learning techniques, specifically PDP, ICE, and ALE, are valuable for understanding GBT models in environmental science.
  • Graphical interpretation tools enhance the visualization and understanding of GBT models but require support from analytical statistical measures.
  • Further methodological research is needed to investigate the properties of interaction tests for GBT models.