Statistical Learning from a Regression Perspective (Springer Texts in Statistics) 2nd Edition by Richard A. Berk | (PDF) Free Download

Description

This textbook considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. This fully revised new edition includes important developments over the past 8 years. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis derives from sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. As in the first edition, a unifying theme is supervised learning that can be treated as a form of regression analysis. Key concepts and procedures are illustrated with real applications, especially those with practical implications. The material is written for upper undergraduate level and graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. The author uses this book in a course on modern regression for the social, behavioral, and biological sciences. All of the analyses included are done in R with code routinely provided.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐Great treatment of machine learning methods. Professor Berk is a practitioner and brings a much needed measure of realism to the study of machine learning. The book goes deeply into the methods discussed and explores how any given procedure can lead to erroneous results with real world data. It also includes discussions on the importance of data collection and management rather than just skip data analysis.A lot of care is placed on understanding and critically analyzing what a particular procedure does and what has been its track record in real applications. The book would be most beneficial for people who have already been exposed to the techniques included in the book and presumes some familiarity with linear algebra (many formulas use matrix notation). For an intro to the techniques I would recommend An Introduction to Statistical Learning with Applications in R by Hastie et al

⭐Explica de modo claro los métodos básicos del machine learning supervisado. No es difícil de leer con si se tiene una base estadística. Un buen libro

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