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When associated with Phoenix VCS, the best classification algorithm is Instance-Based Learning.

Secondly, if only the best classification algorithm is chosen for each subset of selected features, fsCoP ACE2) also performs well similar with fsCoP.

The best classification algorithm was the KNNC classifier, which achieved 90% for sensitivity, 91% for specificity, and an AUC value of 96% with 9% global error.

The best classification algorithm was again the KNNC classifier, which achieved 93% for sensitivity, 94% for specificity, and an AUC value of 98% with 6% global error.

The best classification algorithm was the KNNC classifier, which achieved 88% for sensitivity, 88% for specificity, and an AUC value of 93% with a 12% global error.

The NAFLD classification algorithm is superior to ICD-9 billing data alone.

No rigid classification algorithm is used.

Using the best classification algorithm, there were five optimum SNP subsets selected in the wrapper step in each stratum, corresponding to the 2, 3, 4, 5 or 10-category classification situation, respectively.

It is perhaps surprising that so many of the best-known text classification algorithms are linear.

However, LDA, DLDA, LREG, and QDA classification algorithms were consistently among the best-performing models for each problem.

Five classification algorithms were tested and Support Vector Machine (SVM) demonstrated the best prediction accuracy.