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One means of evaluating the performance of the statistical models is to split the data in half, derive the model from one half of the data and test the model on the other half.
We test the model on a sample of 224 footwear firms.
We successfully test the model on four toy problems and on a real world network.
In this contribution, we test the model on a case study of the dorsal epithelium of the Drosophila melanogaster embryo during early dorsal closure (and, less conspicuously, germband retraction).
We then test the model on the basis of detailed observation and analysis of two case studies, which have successfully created value through commercialization of advanced materials technology.
We test the model on the phantom data based on the real machine tool.
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To test the performance of our method and to compare it with the best available one, we tested the model on a widely used benchmark (Sivasubramanian et al., 2009) (RA dataset), consisting of 53 antibodies.
At last, we test the models on both synthetic and real images.
Several years later, the federal government tested the model on 734 homeless across 11 cities, finding the model dramatically reduced levels of addiction as well as shrank health related costs by half.
We address these aims by (a) modeling an example of insight problem solving and comparing it to empirical data, and (b) testing the model on a robot platform.
Researchers tested the model on a sample of more than 43,000 soldiers who served between 2011 and 2013, and found the most at-risk category were responsible for 51% of violent crimes committed by those soldiers.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com