Titlebook: Validity, Reliability, and Significance; Empirical Methods fo Stefan Riezler,Michael Hagmann Book 2024Latest edition The Editor(s) (if appl

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Book 2024Latest editionffect parameters of LMEMs. Lastly, a significance test based on the likelihood ratios of nested LMEMs trained on the performance scores of two machine learning models is shown to naturally allow the inclusion of variations in meta-parameter settings into hypothesis testing, and further facilitates a

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Stefan Riezler,Michael Hagmanngical studies. It deals with majorcell types studied in the field of toxicology and will be useful foranyone wishing to start work with animal cell cultures or to refreshtheir knowledge relating to .in vitro. cell models. Fundamentalchapters deal with the general biology of cytotoxicity and cellimmo

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Stefan Riezler,Michael Hagmannf specific genes. The action of progesterone is mediated by specific high-affinity PRs which belong to the superfamily of nuclear receptors (Green and Chambon 1986; Evans 1988; O’Malley 1990). Once activated through binding of the hormone, the progesterone receptor (PR) binds to short .-acting regul

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Stefan Riezler,Michael Hagmannus system. The complexity of organization, the heteroge­ neity of cell types and their interactions, and the difficulty of controlling experimental variables in intact organisms make this a formidable task. Because of the ability that it affords to analyze smaller components of the nervous system (e

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Introduction,sed 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 dif