Study: Improving the interpretability of ML features for end-users

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    A taxonomy is created to improve the interpretability of ML features by researchers beacuse we are using state-of-the-art ways of explaining machine-learning models, there is still a lot of confusion stemming from the features.The amount that specific features employed in the model contribute to its prediction is frequently described in explanation techniques that help consumers comprehend and trust machine-learning models. For instance, a doctor may be interested in learning how much the patient’s heart rate data affects a model that forecasts a patient’s

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