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Description: 计算ROC曲线和AUC的Matlab程序
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Size: 1099 |
Author: 刘国亮 |
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Description: 计算ROC曲线和AUC的Matlab程序-Calculation of ROC curves and AUC of Matlab procedures
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Size: 1024 |
Author: 刘国亮 |
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Description: 用C语言自己编写基于特定训练数据和测试数据的SVM程序，并用AUC对其评估-I have written using C language based on the specific training data and test data of the SVM procedure, and their assessment of AUC
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Size: 533504 |
Author: 高如如 |
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Description: ROC curves illustrate performance on a binary classification problem where classification is based on simply thresholding a set of scores at varying levels. Lenient thresholds give high sensitivity but low specificity, strict thresholds give high specificity but low sensitivity the ROC curve plots this trade-off over a range of thresholds (usually with sens vs 1-spec, but I prefer sens vs spec this code gives you the option).
It is theoretically possible to operate anywhere on the convex hull of an ROC curve, so this is plotted too. The area under the curve (AUC) for a ROC plot is a measure of overall accuracy, and the area under the ROCCH is a kind of upper bound on what might be achievable with a weighted combination of differently thresholded results from the given classifier
-ROC curves illustrate performance on a binary classification problem where classification is based on simply thresholding a set of scores at varying levels. Lenient thresholds give high sensitivity but low specificity, strict thresholds give high specificity but low sensitivity the ROC curve plots this trade-off over a range of thresholds (usually with sens vs 1-spec, but I prefer sens vs spec this code gives you the option).
It is theoretically possible to operate anywhere on the convex hull of an ROC curve, so this is plotted too. The area under the curve (AUC) for a ROC plot is a measure of overall accuracy, and the area under the ROCCH is a kind of upper bound on what might be achievable with a weighted combination of differently thresholded results from the given classifier
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Size: 4096 |
Author: saadat |
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Description: 通过决策值可以绘制出ROC曲线的python程序-This tool which gives the ROC (Receiver Operating Characteristic) curve and AUC (Area Under Curve) by ranking the decision values.
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Size: 6144 |
Author: quarryhero |
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Description: 利用matlab实现的基于EM算法的贝叶斯分类器的源代码，可以用来分类或识别，很值得收藏-Using matlab to achieve EM algorithm based on Bayesian classifier of the source code can be used to classification or identification, it is worthy of collection
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Size: 11264 |
Author: satanwings |
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Description: 改进的支持向量机工具，直接输入测试集合训练集，得到训练模型的roc图像和auc值-Improved support vector machine tools, direct input test set training set, obtain training model roc images and auc values
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Size: 19456 |
Author: tamiaode |
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Description: 系统过滤评价指标 AUC以及top-L-precision 以及汉明距离-AUC and evaluation system filter top-L-precision and the Hamming distance
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Size: 6144 |
Author: zhuyuxiao |
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Description: 利用exl数据绘制ROC曲线求AUC与最佳工作点-AUC ROC curve in order to draw
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Size: 1024 |
Author: 吴仪 |
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Description: A good matlab code that analysis the ROC curve and corresponding AUC value to estimate the sensitive and the currectness of the sample estimate. This code is suitable for variable type and function data.
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Size: 3072 |
Author: lihuijia |
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Description: 链路预测中十种局部相似性指标，AUC衡量算法的效率，此代码为十种局部相似性指标AUC程序-AUC of ten local similarity indices
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Size: 5120 |
Author: 郑汉彬 |
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Description: 根据计算的灵敏性特异性，绘制ROC曲线，计算相应的AUC-According to the calculated sensitivity specificity, rendering the ROC curve, calculate the corresponding AUC
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Size: 1024 |
Author: jiaoshuier |
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Description: colAUC calculates Area under ROC curve (AUC)
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Size: 49152 |
Author: KONG WIN |
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Description: HLR/AuC testing gateway for hostapd EAP-SIM/AKA database/authenticator.
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Size: 4096 |
Author: yeipiujeng |
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Description: AUC实现源准备率和召回率。AUC的值介于0.5到1.0之间，较大的AUC代表了较好的Performance。-Area Under roc Curve(AUC)
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Size: 7168 |
Author: fyzl |
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Description: 计算AUC的小程序
ROC的全名叫做Receiver Operating Characteristic，其主要分析工具是一个画在二维平面上的曲线——ROC curve。平面的横坐标是false positive rate(FPR)，纵坐标是true positive rate(TPR)。对某个分类器而言，我们可以根据其在测试样本上的表现得到一个TPR和FPR点对。这样，此分类器就可以映射成ROC平面上的一个点。调整这个分类器分类时候使用的阈值，我们就可以得到一个经过(0, 0)，(1, 1)的曲线，这就是此分类器的ROC曲线。一般情况下，这个曲线都应该处于(0, 0)和(1, 1)连线的上方。因为(0, 0)和(1, 1)连线形成的ROC曲线实际上代表的是一个随机分类器。如果很不幸，你得到一个位于此直线下方的分类器的话，一个直观的补救办法就是把所有的预测结果反向，即：分类器输出结果为正类，则最终分类的结果为负类，反之，则为正类。虽然，用ROC curve来表示分类器的performance很直观好用。可是，人们总是希望能有一个数值来标志分类器的好坏。于是Area Under roc Curve(AUC)就出现了。顾名思义，AUC的值就是处于ROC curve下方的那部分面积的大小。通常，AUC的值介于0.5到1.0之间，较大的AUC代表了较好的performance。好了，到此为止，所有的 前续介绍部分结束，下面进入本篇帖子的主题：AUC的计算方法总结。
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Size: 10454 |
Author: xiaobena |
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Description: Graphic in AUC software in order to insert graph for Arion receiver types
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Size: 282624 |
Author: Osman |
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Description: 链路预测中十种局部相似性指标，AUC衡量算法的效率-Ten local similarity indices in link prediction, the efficiency of AUC algorithm
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Size: 4096 |
Author: zhch68 |
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Description: 不平衡数据分类评价标准，受试者工作曲线下方面积(Unbalance data classification evaluation standard)
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Size: 1024 |
Author: 电风扇大师 |
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Description: 使用SVM分类器来预测乳腺癌病人的预后(特征选择；分类器构建)，评价模型时使用无被交叉验证，性能评价指标包括准确率，AUC，灵敏度，特异度。学会最基本的机器学习方法。可查看分发给大家的代码，以后遇到类似的问题，可用相似的思路和代码。(The SVM classifier was used to predict the prognosis of breast cancer patients (feature selection; classifier construction), and the model was used without cross-validation. Performance evaluation indicators included accuracy, AUC, sensitivity, and specificity. Learn the most basic machine learning methods. You can view the code distributed to everyone, and later encounter similar problems, similar ideas and code can be used.)
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Size: 1024 |
Author: 木葉流光 |
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