書目名稱 | Validity, Reliability, and Significance | 副標(biāo)題 | Empirical Methods fo | 編輯 | Stefan Riezler,Michael Hagmann | 視頻video | http://file.papertrans.cn/981/980278/980278.mp4 | 叢書名稱 | Synthesis Lectures on Human Language Technologies | 圖書封面 |  | 描述 | Empirical methods are means to answering methodological questions of empirical sciences by statistical techniques. The methodological questions addressed in this book include the problems of validity, reliability, and significance. In the case of machine learning, these correspond to the questions of whether a model predicts what it purports to predict, whether a model‘s performance is consistent across replications, and whether a performance difference between two models is due to chance, respectively. The goal of this book is to answer these questions by concrete statistical tests that can be applied to assess validity, reliability, and significance of data annotation and machine learning prediction in the fields of NLP and data science. Our focus is on model-based empirical methods where data annotations and model predictions are treated as training data for interpretable probabilistic models from the well-understood families of generalized additive models (GAMs) and linear mixed effects models (LMEMs). Based on the interpretable parameters of the trained GAMs or LMEMs, the book presents model-based statistical tests such as a validity test that allows detecting circular feature | 出版日期 | Book 2022 | 版次 | 1 | doi | https://doi.org/10.1007/978-3-031-02183-1 | isbn_softcover | 978-3-031-01055-2 | isbn_ebook | 978-3-031-02183-1Series ISSN 1947-4040 Series E-ISSN 1947-4059 | issn_series | 1947-4040 | copyright | Springer Nature Switzerland AG 2022 |
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