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

Aphorism

https://doi.org/10.1007/978-3-030-15002-0 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 requires the interpretation of an analysis.

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音乐等

Introduction to Learning from Data,portance of this, data have been called the “oil of the twenty-first century.” To deal with this flood of data, a new field called data science has been established. This chapter provides a general overview of data science and what learning from data means.

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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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Ankylo-

,Unión Patriótica: the Official Party,s in data science. This will show that there is more than one way to look at prediction models, and that no one is superior to the others. In addition, we present our own pragmatic organization of methods, which consists of a mixture of application-based and model-based categories. Such an approach

Accessible

https://doi.org/10.1007/978-3-030-15002-0 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

发酵

Making Strategic Leaders: Some Reflections,e 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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