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(A posterior probability of a testing data point, A, is estimated by the classification model as the probability that A will be classified as positive, denoted as P(+|A).) As the probability getting lower, the Naïve Bayesain classifier outperforms the SVM classifier, with a larger area under curve.
Furthermore, AUC score obtained by the classification model using these 55 features was 0.8803, indicating that this model has good discriminating power for drug combinations.
Spectra with predicted values greater than the Bayesian threshold are designated as belonging to a particular category, as defined by the classification model.
However, different classification models may highlight different sets of relevant genes, and sometimes genes that might be biologically representative are discarded by the classification model.
Based on the threshold levels indicated by the classification model, a prediction rule was then developed (Table 1) which assigned 1 point for a serum albumin level ≤ 24.5 g/L, 1 point for a CRP level > 228 mg/L and 1 point for a combination of WCC >12 × 10 mcL and respiratory rate > 17 resps/min as this proved more discriminatory than either value alone.
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Once the four steps of the preprocessing phase were completed successfully, the datasets were ready to be classified by applying the classification model described below.
It may also indicate that the relationship between Nugent score BV and the microbial community is more easily captured by the classification models.
All protein peak intensities of samples in the test set were evaluated by BPS using the classification model.
Computer simulation results by them have demonstrated that the classification model provides a sufficiently high classification rate in comparison with that of other models proposed in the literature.
As a consequence the classification model generated by d-Confidence is able of identifying more distinct classes faster.
This is not unusual, since the classification model provided by the decision tree can serve as an explanatory tool to distinguish between objects of different classes [32].
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by the classification analysis
by the cascade model
by the classification discordance
by the classification accuracy
by the classification schema
by the classification result
by the classification tree
by the classification rate
by the classification algorithm
by the material model
by the classification method
by the classification scheme
by the classification routine
by the classification error
by the classification output
by the classification module
by the classification scale
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Justyna Jupowicz-Kozak
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