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
Dropout early warning systems for high school students using machine learning
Jae Young Chung,Sunbok Lee +1 more
TL;DR: The predictive model showed an excellent performance in predicting students' dropouts in terms of various performance metrics for binary classification and the benefit of using machine learning with students' big data in education is demonstrated.
Posted Content
The cave of Shadows. Addressing the human factor with generalized additive mixed models
TL;DR: In this article, generalized additive mixed models are introduced as an extension of the generalized linear mixed model which makes it possible to deal with temporal autocorrelational structure in experimental data.
Proceedings ArticleDOI
Application of Random Forest Algorithm on Feature Subset Selection and Classification and Regression
TL;DR: This paper applied the concept of random forest algorithm on the feature subset selection and classification and regression to perform the comparative study of the random Forest algorithm in different perspectives.
Journal ArticleDOI
A Data Science Course for Undergraduates: Thinking With Data
TL;DR: In this paper, the authors describe an undergraduate course in a liberal arts environment that provides students with the tools necessary to apply data science, which is an emerging interdisciplinary field that combines elements of mathematics, statistics, computer science, and knowledge in particular application domain for the purpose of extracting meaningful information from the increasingly sophisticated array of data available in many settings.
Journal ArticleDOI
Solar PV output prediction from video streams using convolutional neural networks
TL;DR: This work uses convolutional neural networks (CNN) to correlate PV output to contemporaneous images of the sky (a “now-cast”) and explores the sensitivity of model accuracy to a variety of CNN structures, with different widths, depths, and input image resolutions among other hyper-parameters.