Most modern machine learning models are known as black-box models. By default, these predictors don't provide an explanation as to why a certain event or example has been assigned a particular class or value. Model explainability methods aim to interpret the decision-making process of a black-box model and present it in a way that is easy for researchers to understand. These methods can provide local (figuring why a specific input has been assigned a specific output) an global (uncovering general dependencies between input features and the output of the model) explanations. In this talk we will cover several popular model-agnostic explainability methods and compare them in explaining the output of a neural network in the scope of high-energy physics analysis. We will also use a modern high accuracy glass-box machine learning model (Explainable Boosting Machine) and show how its predictions can be used to better understand the data.
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