Название: Machine Learning Toolbox for Social Scientists: Applied Predictive Analytics with RАвтор: Yigit Aydede
Издательство: CRC Press
Год: 2024
Страниц: 601
Язык: английский
Формат: pdf (true), epub
Размер: 35.4 MB
Machine Learning Toolbox for Social Scientists covers predictive methods with complementary statistical "tools" that make it mostly self-contained. The inferential statistics is the traditional framework for most data analytics courses in social science and business fields, especially in Economics and Finance. The new organization that this book offers goes beyond standard Machine Learning code applications, providing intuitive backgrounds for new predictive methods that social science and business students can follow. The book also adds many other modern statistical tools complementary to predictive methods that cannot be easily found in "econometrics" textbooks: nonparametric methods, data exploration with predictive models, penalized regressions, model selection with sparsity, dimension reduction methods, nonparametric time-series predictions, graphical network analysis, algorithmic optimization methods, classification with imbalanced data, and many others. This book delves into the first component, statistical models, without excessive abstraction. It doesn’t cover every aspect of programming, but provides sufficient coding skills for you to build predictive algorithms using R.
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