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The OneR performs evaluation using a simple classification that generates one rule for each predictor in the data and selects the rule with the smallest total error as its "one rule".
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The evaluation used a simple survey design conducted on a sample of pupils from the 7th 9th grades (N = 1219).
Preliminary evaluations using a simple but reliable short-cut method indicated that a 15 component aromatics mixture can be separated very efficiently into four fractions according to the given product specifications employing either a single or a multiple partition wall dividing wall column (DWC).
The first 2N evaluations use a simple Nelder-Mead algorithm to learn about the cost space.
As a result, we can confirm that most evaluations use a simple research design in which, typically, the game is used and afterwards subjective feedback is collected via questionnaires from the learners.
The evaluation used a combination of simple rating scales for satisfaction and free text questions allowing for the collection of rich data.
The first 20 evaluations are an initial training run using a simple Nelder-Mead algorithm.
Using a simple linear additive evaluation model, total scores were calculated for a set of interventions, and rank ordered accordingly: high priority interventions in Ghana were prevention of mother to child transmission in HIV/AIDS control, and treatment of pneumonia and diarrhea in childhood.
To ensure the robustness of these findings, the evaluation of different filtering was also performed using a simple SCRF without derivatives.
The evaluation of the classification accuracy of each method was carried out using a simple k ̌ –NN classifier evaluated following a cross-validation scheme [22].
SNPs were called using a simple thresholding.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com