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. Author manuscript; available in PMC: 2012 Jun 1.
Published in final edited form as: Proteins. 2011 Apr 12;79(6):1952–1963. doi: 10.1002/prot.23020

Figure 6. Performance of the SVM classifier on the benchmark dataset.

Figure 6

A. ROC curve for the SVM classifier (brown) compared to those of linear classifiers for the six individual features. Each point on the SVM curve is an average of ten CV10 experiments and corresponds to a specific probability threshold (PT). Error bars represent the standard deviations from the averages (not shown for all points in order to maintain clarity). B. Corresponding precision-recall curves are generated in the same way as the ROC curves for the above classifiers. C. MCC for SVM as a function of the probability threshold and for the linear classifiers as a function of feature thresholds. The highest MCC for the SVM is 0.32 for a threshold of 0.05. For both B and C, error bars are as in A.