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Titlebook: Elements of Data Science, Machine Learning, and Artificial Intelligence Using R; Frank Emmert-Streib,Salissou Moutari,Matthias Dehm Textbo

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发表于 2025-3-21 19:06:55 | 显示全部楼层 |阅读模式
书目名称Elements of Data Science, Machine Learning, and Artificial Intelligence Using R
编辑Frank Emmert-Streib,Salissou Moutari,Matthias Dehm
视频video
概述Provides students with tools they need to analyze complex data using methods from data science.Presents the tools students need to analyze data using the R programming language.Includes a full suite o
图书封面Titlebook: Elements of Data Science, Machine Learning, and Artificial Intelligence Using R;  Frank Emmert-Streib,Salissou Moutari,Matthias Dehm Textbo
描述The textbook provides students with tools they need to analyze complex data using methods from data science, machine learning and artificial intelligence. The authors include both the presentation of methods along with applications using the programming language R, which is the gold standard for analyzing data. The authors cover all three main components of data science: computer science; mathematics and statistics; and domain knowledge. The book presents methods and implementations in R side-by-side, allowing the immediate practical application of the learning concepts. Furthermore, this teaches computational thinking in a natural way. The book includes exercises, case studies, Q&A and examples..
出版日期Textbook 2023
关键词Data science; Algorithms; data driven sciences; Learning from data; Prediction models; Bayesian analysis
版次1
doihttps://doi.org/10.1007/978-3-031-13339-8
isbn_softcover978-3-031-13341-1
isbn_ebook978-3-031-13339-8
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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General Error Measures start by introducing four so-called fundamental errors from which most error measures are derived. Then, we discuss 14 different error measures. Finally, we discuss the evaluation of the outcome of a single method and that of multiple methods, showing that such an evaluation is a complex task that
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Resampling Methodse different from the other methods presented in this book. As we will see, resampling and subsampling methods allow the generation of “new” data sets from any given data set, which can then be used either for the assessment of a prediction model or for the estimation of parameters.
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Statistical Inferencen reality, a sample of data always has a finite size, any conclusions reached about the population are always uncertain to a degree. The goal of statistics is to quantify the amount of uncertainty around the conclusions that are made based on a sample of data. In general, . is the (systematic) proce
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Dimension Reductiont or non-informative, which generally hinders the ability of most machine learning algorithms to perform efficiently. A common approach to address these issues is to check whether a low-dimensional structure can be detected within these high-dimensional data. If the answer is yes, then we can identi
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Classificationuss aspects common to general classification methods. This includes an extension of measures for binary decision-making to multi-class classification problems. As we will see, this extension is not trivial, because the contingency table becomes multi-dimensional when conditioned on different classes
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Hypothesis Testingriginated from statistics, hypothesis testing has complex interdependencies between its procedural components, which makes it hard to thoroughly comprehend. In this chapter, we discuss the underlying logic behind statistical hypothesis testing and the formal meaning of its components and their conne
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