Classification
In this chapter, we consider constructing a classification rule from covariates to a response that takes values from a finite set such as \(\pm 1\) , figures \(0,1,\ldots ,9\) . For example, we wish to classify a postal code from handwritten characters an
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Statistical Learning with Math and R 100 Exercises for Building Logic
Statistical Learning with Math and R
Joe Suzuki
Statistical Learning with Math and R 100 Exercises for Building Logic
123
Joe Suzuki Graduate School of Engineering Science Osaka University Toyonaka, Osaka, Japan
ISBN 978-981-15-7567-9 ISBN 978-981-15-7568-6 https://doi.org/10.1007/978-981-15-7568-6
(eBook)
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Preface
I am currently with the Statistics Laboratory at Osaka University, Japan. I often meet with data scientists who are engaged in machine learning and statistical analyses for research collaborations and introducing my students to them. I recently found out that almost all of them think that (mathematical) logic rather than knowledge and experience is the most crucial ability for grasping the essence in their jobs. Our necessary knowledge is changing every day and can be obtained when needed. However, logic allows us to examine whether each item on the Internet is correct and follow any changes; without it, we might miss even chances. In 2016, I started teaching statistical machine learning to the undergraduate students of the Mathematics Department. In the beginning, I was mainly teaching them what (statistical) machine learning (ML) is and how to use it. I explained the procedures of ML, such as logistic regression, support vector machines, k-means clustering, etc., by showing figures and providing intuitive explanations. At the same time, the students tried to understand ML by guessing the
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