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
Forecasting Corn Yield With Machine Learning Ensembles
TL;DR: In this article, a machine leaning based framework was proposed to forecast corn yields in three US Corn Belt states (Illinois, Indiana, and Iowa) considering complete and partial in-season weather knowledge.
Journal ArticleDOI
Machine learning approaches for anomaly detection of water quality on a real-world data set*
TL;DR: The results show that all algorithms are vulnerable although SVM, ANN and logistic regressions tend to be a little less vulnerable, while DNN, RNN and LSTM are very vulnerable.
Journal ArticleDOI
Adaptive invasive species distribution models: a framework for modeling incipient invasions
TL;DR: A framework for adaptive, niche-based, invasive species distribution model (iSDM) development and utilization is developed and useful for advancing coordinated invasive species modeling, management and monitoring from local scales to the regional, continental and global scales at which biological invasions occur and harm native ecosystems and economies.
Journal ArticleDOI
Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS
TL;DR: The proposed ensemble learner post-processes simulations of the GR4J hydrological model for 511 basins in the contiguous US is illustrated with significantly improved performance relative to the base-learners used and a less prominent improvementrelative to the “hard to beat in practice” equal-weight combiner.
Journal ArticleDOI
S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts
Judah Cohen,Dim Coumou,Dim Coumou,Jessica Hwang,Lester Mackey,Paulo Orenstein,Sonja Totz,Eli Tziperman +7 more
TL;DR: It is suggested that S2S prediction will be most beneficial to the public by incorporating mixed or a hybrid of dynamical forecasts and updated statistical techniques such as machine learning.