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' (Greenwell 2017) <doi:10.32614/RJ-2017-016>, 'ALEPlot' (Apley 2018) <arXiv:1612.08468> and 'factorMerger' (Sitko and Biecek 2017) <arXiv:1709.04412>. The single_prediction() explainer attributes parts of a model prediction to particular variables used in the model. It is a wrapper over 'breakDown' package (Staniak and Biecek 2018) <doi:10.32614/RJ-2018-072>. The variable_dropout() explainer calculates variable importance scores based on variable shuffling (Fisher at al. 2018) <arXiv:1801.01489>. All these explainers can be plotted with generic plot() function and compared across different models. 'DALEX' is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.
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