Supervised Machine Learning For Science How To Stop Worrying And Love Your Black Box Christoph Molnar by Christoph Molnar, Timo Freiesleben 9783911578004, 3911578008 instant download after payment.

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