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@ -23,17 +23,15 @@ for epsilon = min(pval):stepsize:max(pval)
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% Note: You can use predictions = (pval < epsilon) to get a binary vector
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% Note: You can use predictions = (pval < epsilon) to get a binary vector
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% of 0's and 1's of the outlier predictions
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% of 0's and 1's of the outlier predictions
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predictions = (pval < epsilon);
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tp = sum((predictions == 1) & (yval == 1));
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fp = sum((predictions == 1) & (yval == 0));
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fn = sum((predictions == 0) & (yval == 1));
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prec = tp/(tp+fp);
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rec = tp/(tp+fn);
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F1 = (2*prec*rec)/(prec+rec);
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% =============================================================
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% =============================================================
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