R package DALEX: Descriptive mAchine Learning EXplanations. Machine Learning (ML) models are widely used and have various applications in classification or regression. Models created with boosting, bagging, stacking or similar techniques are often used due to their high performance, but such black-box models usually lack of interpretability. DALEX package contains various explainers that help to understand the link between input variables and model output. The single_variable() explainer extracts conditional response of a model as a function of a single selected variable. It is a wrapper over packages ’pdp’ and ’ALEPlot’. The single_prediction() explainer attributes parts of a model prediction to particular variables used in the model. It is a wrapper over ’breakDown’ package. The variable_dropout() explainer calculates variable importance scores based on variable shuffling. All these explainers can be plotted with generic plot() function and compared across different models.

References in zbMATH (referenced in 12 articles , 1 standard article )

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  1. Akalin, Altuna: Computational genomics with R. With the assistance of Verdan Franke, Bora Uyar and Jonathan Ronen (2021)
  2. Arya, Vijay; Bellamy, Rachel K. E.; Chen, Pin-Yu; Dhurandhar, Amit; Hind, Michael; Hoffman, Samuel C.; Houde, Stephanie; Liao, Q. Vera; Luss, Ronny; Mojsilović, Aleksandra; Mourad, Sami; Pedemonte, Pablo; Raghavendra, Ramya; Richards, John T.; Sattigeri, Prasanna; Shanmugam, Karthikeyan; Singh, Moninder; Varshney, Kush R.; Wei, Dennis; Zhang, Yunfeng: AI Explainability 360: an extensible toolkit for understanding data and machine learning models (2020)
  3. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  4. Gero Szepannek: An Overview on the Landscape of R Packages for Credit Scoring (2020) arXiv
  5. Hubert Baniecki, Wojciech Kretowicz, Piotr Piatyszek, Jakub Wisniewski, Przemyslaw Biecek: dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python (2020) arXiv
  6. Szymon Maksymiuk, Alicja Gosiewska, Przemyslaw Biecek: Landscape of R packages for eXplainable Artificial Intelligence (2020) arXiv
  7. Delicado, Pedro: Comments on “Data science, big data and statistics” (2019)
  8. Hubert Baniecki; Przemyslaw Biecek: modelStudio: Interactive Studio with Explanations for ML Predictive Models (2019) not zbMATH
  9. Przemysław Biecek, Magda Tatarynowicz, Kamil Romaszko, Mateusz Urbański : modelDown: automated website generator with interpretable documentation for predictive machine learning models (2019) not zbMATH
  10. Aleksandra Grudziaz, Alicja Gosiewska, Przemyslaw Biecek: survxai: an R package for structure-agnostic explanations of survival models (2018) not zbMATH
  11. Alicja Gosiewska; Przemyslaw Biecek: auditor: an R Package for Model-Agnostic Visual Validation and Diagnostic (2018) arXiv
  12. Biecek, Przemysław: DALEX: explainers for complex predictive models in \textttR (2018)