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With the built decision tree, the proposed method classifies test samples.
Our solution estimates degrees of similarity among structural elements from heterogeneous log data, constructs combined Bayesian network (CBN), uses similarity based learning algorithm to compute probabilities in CBN, and classifies test log data into most probable fault categories based on the generated CBN.
This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001).
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Columns 2 3 of Table 6 show the classification performances of the TNB and LCMNB classifiers on classifying test ligands for each of the six target proteins in the dataset.
For completeness we also looked into the classification performances of the TNB and LCMNB classifiers on classifying test ligands for each of the 31 target proteins in this dataset.
We also looked into the classification performances of the TNB and LCMNB classifiers on classifying test ligands for each of the 23 target proteins in this dataset.
Through a battery of 93 questions, it classifies test-takers into one of 16 personality types based on four sets of binary characteristics: introvert/extrovert, intuitive/sensory, feeling/thinking and judging/perceiving.
The disease prediction aspect classifies testing samples with our pre-defined disease prediction model.
In this section, instead of using the number of correctly classified test documents (or, equivalently, the error rate on test documents) as evaluation measure, we adopt an evaluation measure that addresses the inherent uncertainty of labeling.
In the present study, using such attributes and their results computed we classified test cases into groups of healthy subjects and patients with multilayer perceptron neural networks.
Classifying test items with the F/NF and T/NT dichotomies has elements of subjectivity in it, and therefore we use a methodology with groups of scorers and inter-scorer reliability.
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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