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A common task within machine learning is to train a model to predict an unknown outcome (response variable) based on a set of known input variables/features. When using such models for real life applications, it is often crucial to understand why a certain set of features lead to a specific prediction. Most machine learning models are, however, complicated and hard to understand, so that they are often viewed as “black-boxes”, that produce some output from some input.

shapr: An R-package for explaining machine learning models with dependence-aware Shapley values