Best models predicting high-impact species using a statistical learning approach.

Model weighting assumption was tested by comparing true positives and false negatives equally (w = 0.5) (comparable to Table 2) and weighting true positives more heavily than false negatives) (w = 0.9). Weuc is expressed as a proportion of the maximum possible value given the value of w, thus in both cases a perfect classifier would have a Weuc of 0, and a classifier that is guessing randomly will have a Weuc of 1.

我们通过两种权重设置对模型权重假设进行了检验：一是将真阳性（true positives）与假阴性（false negatives）赋予同等权重（w = 0.5，与表2的设置可比），二是赋予真阳性更高的权重（w = 0.9）。Weuc指标以给定权重w下的最大可能值的比例形式呈现，因此在上述两种权重设置中，完美分类器的Weuc值均为0，而随机猜测的分类器的Weuc值均为1。