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
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Journal ArticleDOI
Meningioma DNA methylation groups identify biological drivers and therapeutic vulnerabilities
Abrar Choudhury,Stephen T. Magill,Charlotte D. Eaton,Briana C. Prager,William C. Chen,Martha A. Cady,Kyounghee Seo,Calixto-Hope G Lucas,T. Casey-Clyde,Harish N. Vasudevan,S. John Liu,Javier Villanueva-Meyer,Tai Chung Lam,Jenny Kan-suen Pu,Lai-Fung Li,Gilberto K.K. Leung,Danielle L. Swaney,Michael Zhang,Jason Chan,Zhixin Qiu,Michael Martin,Matthew S. Susko,Steve Braunstein,Nancy Ann Oberheim Bush,Jessica Schulte,Nicholas Butowski,Penny K. Sneed,Mitchel S. Berger,Nevan J. Krogan,Arie Perry,T. J. Phillips,David A. Solomon,Joseph F. Costello,Michael P. McDermott,Jeremy N. Rich,David R. Raleigh +35 more
TL;DR: In this paper , DNA methylation profiling of 565 meningiomas highlights three groups associated with distinct molecular, clinical and therapeutic features: immune-enriched, hypermitotic and Merlin-intact meningus.
Posted Content
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use
TL;DR: In this article, the authors surveyed clinicians from two distinct acute care specialties (Intenstive Care Unit and Emergency Department) to identify specific aspects of explainability that may catalyze building trust in ML models.
Journal ArticleDOI
Learning Roadway Surface Disruption Patterns Using the Bag of Words Representation
TL;DR: Experimental results reveal that the representation technique boosts considerably the classification performance when compared with the state of the art solutions, reducing in one order of magnitude the false-positives/negatives rate and surpassing the classification accuracy for about 10% in a multiclass data set.
Journal ArticleDOI
Feature subset selection for logistic regression via mixed integer optimization
TL;DR: The computational results demonstrate that when the number of candidate features was less than 40, the method successfully provided a feature subset that was sufficiently close to an optimal one in a reasonable amount of time.
Journal ArticleDOI
The Gulf of Aden Intermediate Water Intrusion Regulates the Southern Red Sea Summer Phytoplankton Blooms.
TL;DR: This work provides the first detailed description of their spatiotemporal distribution and reports the mechanisms regulating them, and provides statistical evidence that the subsurface intrusion plays a key role in the development of the southern Red Sea phytoplankton blooms.