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By varying the architectures of DIMLP networks we found that the average predictive accuracy of rules, as well as their complexity were quite stable.
In CPAR, Laplace accuracy is used to measure the accuracy of rules.
c and s stand for confidence and support to measure accuracy of rules.
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Some numerical examples are provided to illustrate the accuracy of proposed rules and comparison between them.
Predictive apriori is an algorithm proposed by [27], which exploits the accuracy of association rules, on hidden data.
The accuracy of these rules can be assessed with percentages of correct classification or odds-ratios that are easily understood.
Another set of observations, known as test set is used to verify the quality and accuracy of the rules.
In overall, the paper aims to stress the fact that consistent, robust and efficient nonlinear 3-D finite element models could improve and assess the accuracy of design rules specified in current codes of practice at ambient and elevated temperatures.
The choice of the normalization procedure may greatly affect the classification accuracy of prediction rules, perhaps even more than the choice of the classifier.
The overall accuracy of these rules was compared using the mean and standard deviation of 100 AUC values per model by using the corrected, resampled t-test [ 36].
We order these rules in decreasing order of accuracy, where the accuracy of a rule is defined as the number of times the rule yields the correct temporal relation type divided by the number of times it is applied, as measured on the training documents.
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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