特异度敏感度.docxVIP

真阳性TP——金标准认定患有某种疾病，并且试验也认为患有某种疾病；真阴性TN——金标准认定不患有某种疾病，并且试验也认为不患有该种疾病的情况；假阳性FP——金标准认定不患有某种疾病，但是试验认为患有该种疾病；假阴性FN——金标准认定患有某种疾病，但是试验认为不患有该种疾病。TP：与金标准一样的TN：不是这个病FP：误诊FN：漏诊正确率（Accuracy）又称符合率，是试验所检出的真阳性和真阴性例数之和占总例数的百分比。它反映试验结果与金标准诊断结果的符合程度。计算方法为（TP+TN）/（TP+TN+FP+FN）。灵敏度（Sensitivity），又称真阳性率，召回率，指金标准确诊的病例被试验判为阳性的百分比。它可衡量试验正确地识别患某病者的能力。计算方法为TP/(TP+FN)。召回率又称查全率，是指正确是被的实体个数占测试集中所有实体个数的比例。漏诊率，又称假阴性率（False negative rate），指金标准确诊的病例被试验错判为阴性的百分比。漏诊率=1-灵敏度。特异度（Specificity），又称真阴性率，指金标准确诊的非病例被试验判为阴性的百分比。它可衡量试验正确地识别非病例的能力。计算方法为TN/(FP+TN)。或1-假阳性率。它反映筛检试验确定非病人的能力。误诊率，又称假阳性率（false positive rate），指金标准确诊的非病例被试验错判为阳性的百分比。误诊率=1-特异度。Precision = TP/TP+FP, 精度是真属于x类的元素在所有属于x类的元素中的比例。精度是指正确识别的实体个数占所有被识别实体个数的比例。正确识别的个数/正确识别的个数+错误识别的个数F值（F-measure）通过精度和召回率的加权平均，将前两个度量值融为一个度量值，其中β是精度和召回率的相对权重。当β1时，精度比召回率重要；当β1时，召回率比精度重要。一般情况下，取β=1，即认为精度和召回率同等重要。The confusion matrix is more commonly named contingency table. In our case we have two classes, and therefore a 2x2 confusion matrix, the matrix could be arbitrarily large. The number of correctly classified instances is the sum of diagonals in the matrix; all others are incorrectly classified (class a gets misclassified as b exactly twice, and class b gets misclassified as a three times).The True Positive (TP) rate is the proportion of examples which were classified as class x, among all examples which truly have class x, i.e. how much part of the class was captured. It is equivalent to Recall. In the confusion matrix, this is the diagonal element divided by the sum over the relevant row, i.e. 7/(7+2)=0.778 for class yes and 2/(3+2)=0.4 for class no in our example.真阳性率=召回率=灵敏度，是被分成x类的元素在所有例子中真属于x类的比例The False Positive (FP) rate is the proportion of examples which were classified as class x, but belong to a different class, among all examples which are not of class x. In the matrix, this is the column sum of class x minus the diagonal element, divided by the rows sums of all other classes; i.e. 3/5=0.6 for class yes and 2/9=0.222 for class no.假阳性率是被分成属于x类的，但是实际属