Titlebook: An Introduction to Statistical Learning; with Applications in Gareth James,Daniela Witten,Robert Tibshirani Textbook 2021Latest edition The

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Moving Beyond Linearity,So far in this book, we have mostly focused on linear models. Linear models are relatively simple to describe and implement, and have advantages over other approaches in terms of interpretation and inference.

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Support Vector Machines,In this chapter, we discuss the support vector machine (SVM), an approach for classification that was developed in the computer science community in the 1990s and that has grown in popularity since then.

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Survival Analysis and Censored Data,In this chapter, we will consider the topics of . and .. These arise in the analysis of a unique kind of outcome variable: the ..

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Unsupervised Learning,Most of this book concerns . methods such as regression and classification. In the supervised learning setting, we typically have access to a set of . features . measured on . observations, and a response . also measured on those same . observations. The goal is then to predict . using

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Multiple Testing,Thus far, this textbook has mostly focused on estimation and its close cousin, prediction. In this chapter, we instead focus on hypothesis testing, which is key to conducting inference. We remind the reader that inference was briey discussed in Chapter 2.

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