BookDOI
An introduction to statistical learning
TLDR
An introduction to statistical learning provides an accessible overview of the essential toolset for making sense of the vast and complex data sets that have emerged in science, industry, and other sectors in the past twenty years.Abstract:
Statistics An Intduction to Stistical Lerning with Applications in R An Introduction to Statistical Learning provides an accessible overview of the fi eld of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fi elds ranging from biology to fi nance to marketing to astrophysics in the past twenty years. Th is book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classifi cation, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fi elds, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical soft ware platform. Two of the authors co-wrote Th e Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. Th is book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. Th e text assumes only a previous course in linear regression and no knowledge of matrix algebra.read more
Citations
More filters
Journal ArticleDOI
Machine Learning Techniques for the Segmentation of Tomographic Image Data of Functional Materials
Orkun Furat,Mingyan Wang,Matthias Neumann,Lukas Petrich,Matthias Weber,Carl E. Krill,Volker Schmidt +6 more
TL;DR: A quantitative comparison between segmentation results indicates that the 3D U-Net performs best among the considered U-nets, where the latter was trained at a lower resolution due to memory limitations.
Journal ArticleDOI
Cluster analysis and prediction of residential peak demand profiles using occupant activity data
TL;DR: How a novel clustering approach to high-resolution electricity and occupant time-use data from UK households can inform more targeted strategies for residential peak demand reduction and response interventions as well as improve the understanding of constraints and opportunities for demand-side flexibility in the residential sector is discussed.
Journal ArticleDOI
The Double-Edged Sword of Big Data in Organizational and Management Research: A Review of Opportunities and Risks
TL;DR: While many disciplines embrace the possibilities that Big Data present for advancing scholarship and practice, organizational and management research has yet to realize Big Data's potential as mentioned in this paper, which is a concern of many researchers.
Journal ArticleDOI
Contrasting emergence of Lyme disease across ecosystems
Atle Mysterud,William Ryan Easterday,Vetle Malmer Stigum,Anders Bjørnsgaard Aas,Erling L. Meisingset,Hildegunn Viljugrein +5 more
TL;DR: It is shown that both high spatial and temporal deer population density increase Lyme disease incidence, however, the trajectories of deer population sizes play an overall limited role for the recent emergence of the disease.
Journal ArticleDOI
Forecasting China’s regional energy demand by 2030: A Bayesian approach
Xiao-Chen Yuan,Xiao-Chen Yuan,Xun Sun,Xun Sun,Weigang Zhao,Zhifu Mi,Bing Wang,Bing Wang,Yi-Ming Wei +8 more
TL;DR: Li et al. as mentioned in this paper employed a hierarchical Bayesian approach to present the probabilistic forecasts of energy demand at the provincial and national levels, which is effective for energy forecasting by taking model uncertainty, regional heterogeneity, and cross-sectional dependence into account.