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Generalization dissertation

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From: Drew K.
Category: save water
Added: 07.04.2021
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There are a number of different levels of replication discussed in the literature. As discussed, it is not just about duplication. In this case, we are interested in generalisation , which whilst involving duplication, goes further. In the sections that follow, we explain a what generalisation is and b how to decide what type of generalisation to test.
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"A Generalization of The Partition Problem in Statistics" by Jie Zhou

Jie Zhou Follow. In this dissertation, the problem of partitioning a set of treatments with respect to a control treatment is considered. Starting in 's a number of researchers have worked on this problem and have proposed alternative solutions. In Tong , the authors proposed a formulation to solve this problem and hundreds of researchers and practitioners have used that formulation for the partition problem. In this case, the indifference zone gets quite large and the undesirable feature of the Tong's formulation to partition the populations in the indifference zone, without any penalty, can potentially lead Tong's formulation to produce misleading partitions. For this generalized partition, a fully sequential and a two-stage procedure is proposed and its theoretical properties are derived. The proposed procedures are also studied via Monte Carlo Simulation studies.
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Examples of Generalization

Please use the article, Generalizability — The trees, the forest, and the low-hanging fruit by Walter A. The article can be found at the bottom of the week 14 module. Please turn this article into a PowerPoint or Prezi presentation of at least 8 slides of content not including the title slide and the final slide with your APA reference.
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Due to the prevalence of machine learning algorithms and the potential for their decisions to profoundly impact billions of human lives, it is crucial that they are robust, reliable, and understandable. This thesis examines key theoretical pillars of machine learning surrounding generalization and overfitting, and tests the extent to which empirical behavior matches existing theory. We develop novel methods for measuring overfitting and generalization, and we characterize how reproducible observed behavior is across differences in optimization algorithm, dataset, task, evaluation metric, and domain.
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