Titlebook: Information-Driven Machine Learning; Data Science as an E Gerald Friedland Textbook 2024 The Editor(s) (if applicable) and The Author(s), u

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Explainability,the decisions and predictions made by the model (Gilpin et al. (Explaining explanations: An overview of interpretability of machine learning, . pp. 80–89, 2018)). It contrasts with the “black box” concept in machine learning (see Chap. . where even its designers cannot explain why a model arrived at

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LOps, Data drift, bias.This groundbreaking book transcends traditional machine learning approaches by introducing information measurement methodologies that revolutionize the field...Stemming from a UC Berkeley seminar on experimental design for machine learning tasks, these techniques aim to overco

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Textbook 2024he field...Stemming from a UC Berkeley seminar on experimental design for machine learning tasks, these techniques aim to overcome the ‘black box‘ approach of machine learning by reducing conjectures such as magic numbers (hyper-parameters) or model-type bias. Information-based machine learning enab

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