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As can be seen, the classifier had an average accuracy of 81.88 % in the classification of the validation sets, i.e, the average rate of misclassification was <20%.
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Cross-validation yielded a performance of 95% in the classification of the five hedonic sub-classes.
The classifications of the selected cases tested by cross-validation showed that 68.3% of these classifications were correct.
This profile enabled the classification of nonresponders with 100% accuracy in a small validation group (sensitivity and specificity of 100% for predicting nonresponders).
We are not aware of published validation data on the classification of incident breast cancer in the hospital discharge registries.
This allows the classification of soil types based on the clusters in the MDS space, after expert validation.
The classification of solutions includes guidelines for selection of appropriate solution to be employed in field experiments and numerical validation as well.
Feature selection was used to identify a set demonstrating the optimal 98% accuracy of the original classification in the validation sample analysis.
We then recorded the total number of correct classifications in the validation group as well as the total number of correct classification in the training group.
Three classification algorithms, NB, BN and NN, were compared in terms of the cross-validation classification accuracy of the chosen subset of SNPs.
Although this pilot study suggests it has diagnostic potential in the automated classification of melanoma from non-melanoma, prospective studies are required for validation.
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