Supervised Machine Learning for Science : How to stop worrying and love your black box

Christoph Molnar

ISBN 10: 3911578008 / ISBN 13: 9783911578004

Editore: Christoph Molnar, 2024

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nach der Bestellung gedruckt Neuware - Printed after ordering - Machine learning has revolutionized science, from folding proteins and predicting tornadoes to studying human nature. While science has always had an intimate relationship with prediction, machine learning amplified this focus. But can this hyper-focus on prediction models be justified Can a machine learning model be part of a scientific model Or are we on the wrong track In this book, we explore and justify supervised machine learning in science. However, a naive application of supervised learning won't get you far because machine learning in raw form is unsuitable for science. After all, it lacks interpretability, uncertainty quantification, causality, and many more desirable attributes. Yet, we already have all the puzzle pieces needed to improve machine learning, from incorporating domain knowledge and ensuring the representativeness of the training data to creating robust, interpretable, and causal models. The problem is that the solutions are scattered everywhere.In this book, we bring together the philosophical justification and the solutions that make supervised machine learning a powerful tool for science.After the introduction, the book consists of two parts:Part 1 justifies the use of machine learning in science.Part 2 discusses how to integrate machine learning into science.

Codice articolo 9783911578004

Dati bibliografici

• Titolo: Supervised Machine Learning for Science : How to stop worrying and love your black box
• Editore: Christoph Molnar
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 3911578008
• ISBN 13: 9783911578004
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 229 L x 152 A x 16 P (millimetri) 229 millimetri di larghezza per 152 millimetri di altezza per 16 millimetri di profondità
• Peso: 404 grammi

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Riassunto

Machine learning has revolutionized science, from folding proteins and predicting tornadoes to studying human nature. While science has always had an intimate relationship with prediction, machine learning amplified this focus. But can this hyper-focus on prediction models be justified? Can a machine learning model be part of a scientific model? Or are we on the wrong track?

In this book, we explore and justify supervised machine learning in science. However, a naive application of supervised learning won’t get you far because machine learning in raw form is unsuitable for science. After all, it lacks interpretability, uncertainty quantification, causality, and many more desirable attributes. Yet, we already have all the puzzle pieces needed to improve machine learning, from incorporating domain knowledge and ensuring the representativeness of the training data to creating robust, interpretable, and causal models. The problem is that the solutions are scattered everywhere.

In this book, we bring together the philosophical justification and the solutions that make supervised machine learning a powerful tool for science.

After the introduction, the book consists of two parts:

• Part 1 justifies the use of machine learning in science.
• Part 2 discusses how to integrate machine learning into science.