Supervised Machine Learning for Science

<p>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?</p><p>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.</p><p>In this book we bring together the philosophical justification and the solutions that make supervised machine learning a powerful tool for science.</p><p>After the introduction the book consists of two parts:</p><p>Part 1 justifies the use of machine learning in science.</p><p>Part 2 discusses how to integrate machine learning into science.</p>