(redirected fromArea under the curve) Thesaurus Medical Legal Financial Acronyms Encyclopedia Wikipedia AUC orauc abbr.Latin 1.ab urbe condita (from the founding of the city [of Rome, traditionally regarded as 753bc]) 2.anno urbis conditae (in the year from the founding of the city [of ...
AUC(Area u..1、roc曲线:接收者操作特征(receiveroperating characteristic),roc曲线上每个点反映着对同一信号刺激的感受性。横轴:负正类率(false postive rat
Area Under CurveSynonymsSynonymsAUCDefinitionDefinitionThe area under curve (AUC) statistic is an empiricalmeasure of classification performance based on the area under an ROCcurve. It evaluates the performance of aClaude SammutGeoffrey I. Webb
Area under curve-receiver operating characteristics (AUC-ROC) is equivalent to a simple average of the ranks of the actives; the good performance ofearly recognitionsis offset quickly bylate recognitions[76]. Letnbe the number of actives andNbe the total number of compounds; in that case, AUC...
spiral(curve) 螺线 a art. 1.(非特指的)一(个) 2.(同类事物中)任何一个 3.每一(个) 4.某一种 5.(用于某些物质名词前)一种 6.(=the same)同一(个) 7.(与否定词连用, under chassis 起落架,底盘,机脚 最新单词 cast sth into the shade的中文解释 使某事黯然失色 cast sth in sb's ...
AUC顾名思义,area under the curve,曲线的面积,而这条曲线叫ROC(Receiver Operator Characteristic),中文译名很多,“接收机操作特性曲线”,“受试者工作特征曲线"。 ROC曲线的横轴是False Positive Rate(False Alarm Rate),中文译名“假阳率”,“虚警概率”、“伪阳性率”,纵轴是True Positive Rate(Detection Rate...
1. AUC (Area Under Curve) 被定义为ROC曲线下的面积,取值范围一般在0.5和1之间。 使用AUC值作为评价标准是因为很多时候ROC曲线并不能清晰的说明哪个分类器的效果更好,而作为一个数值,对应AUC更大的分类器效果更好。 2.AUC 的计算方法 非参数法:(两种方法实际证明是一致的) 梯形法则:早期由于测试样本有限,我们...
Exercise - Tune the area under the curveCompleted 100 XP 12 minutes The sandbox for this module is currently unavailable. We're working to resolve this as quickly as possible. In the meantime, you may be able to complete this module's exercises using your personal subscription, but charges...
AUC通过P-R曲线表示，P-R曲线横轴为召回率（Recall），纵轴为精确率（Precision）。召回率（Recall）= TP/(TP+FN)，精确率（Precision）= TP/(TP+FP)。P-R曲线越往右上凸，表示预测效果越好。衡量指标是F1值，计算方式为F1=2*P*R/(P+R)。当类别数量变化时，AUC指标比P-R曲线更稳定，减少了...
AUC(Area under Curve):Roc曲线下的面积,介于0.1和1之间。Auc作为数值可以直观的评价分类器的好坏,值越大越好。 首先AUC值是一个概率值,当你随机挑选一个正样本以及负样本,当前的分类算法根据计算得到的Score值将这个正样本排在负样本前面的概率就是AUC值,AUC值越大,当前分类算法越有可能将正样本排在负样本前面...