auc

3-class AUC calculation in R (pROC package)

由 强颜欢笑 提交于 2019-12-24 03:18:06
问题 I met a problem of 3-class ROC analysis in R and obtained a very annoying result (see here ). Now I try to use a different way to solve it. The data is iris and the classifier is multinomial logistic regression which is in nnet package. The code is below: # iris data (3-class ROC) library(nnet) library(pROC) # should be installed first: install.packages('pROC') data(iris) # 3-class logistic regression model = multinom(Species~., data = iris, trace = F) # confusion matrix (z1) & accuracy (E1)

How to plot a ROC curve from Classification Tree probabilities

由 落爺英雄遲暮 提交于 2019-12-24 03:14:17
问题 I am attempting to plot a ROC curve with classification trees probabilities. However, when I plot the curve, it is absent. I am trying to plot the ROC curve and then find the AUC value from the area under the curve. Does anyone know how to fix this? Thank you if you can. The binary column Risk stands for risk misclassification, which I presume is my label. Should I be applying the ROC curve equation at a different point in my code? Here is the data frame: library(ROCR) data(Risk.table) pred =

Difference in average AUC computation using ROCR and pROC (R)

由 风格不统一 提交于 2019-12-23 17:32:02
问题 I am working with cross-validation data (10-fold repeated 5 times) from a SVM-RFE model generated with the caret package. I know that caret package works with pROC package when computing metrics but I need to use ROCR package in order to obtain the average ROC. However, I noticed that the average AUC values were not the same when using each package, so I am not sure if I should use both packages indistinctively. The code I used to prove that is: predictions_NG3<-list() labels_NG3<-list()

Tensorflow 1.4 tf.metrics.auc for AUC calculation

由 谁说我不能喝 提交于 2019-12-23 05:35:08
问题 I am trying to log AUC during training time of my model. According to the documentation, tf.metric.auc needs a label and predictions , both of same shape. But in my case of binary classification, label is a one-dimensional tensor, containing just the classes. And prediction is two-dimensional containing probability for each class of each datapoint. How to calculate AUC in this case? 回答1: Let's have a look at the parameters in the function tf.metrics.auc: labels : A Tensor whose shape matches

How does sklearn actually calculate AUROC?

由 浪子不回头ぞ 提交于 2019-12-23 04:43:40
问题 I understand that the ROC curve for a model is constructed by varying the threshold (that affects TPR, FPR). Thus my initial understanding is that, to calculate the AUROC, you need to run the model many times with different threshold to get that curve and finally calculate the area. But it seems like you just need some probability estimate of the positive class, as in the code example in sklearn's roc_auc_score below, to calculate AUROC. >>> import numpy as np >>> from sklearn.metrics import

AUC(Area under Curve Roc曲线下面积）

由 和自甴很熟 提交于 2019-12-21 08:27:44
一、roc曲线 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 rate FPR)特异度，划分实例中所有负例占所有负例的比例；(1-Specificity) 纵轴：真正类率(true postive rate TPR)灵敏度，Sensitivity(正类覆盖率) 2针对一个二分类问题，将实例分成正类(postive)或者负类(negative)。但是实际中分类时，会出现四种情况. (1)若一个实例是正类并且被预测为正类，即为真正类(True Postive TP) (2)若一个实例是正类，但是被预测成为负类，即为假负类(False Negative FN) (3)若一个实例是负类，但是被预测成为正类，即为假正类(False Postive FP) (4)若一个实例是负类，但是被预测成为负类，即为真负类(True Negative TN) TP:正确的肯定数目 FN:漏报，没有找到正确匹配的数目 FP:误报，没有的匹配不正确 TN:正确拒绝的非匹配数目 列联表如下，1代表正类，0代表负类： 由上表可得出横，纵轴的计算公式： (1)真正类率(True Postive Rate)TPR: TP/(TP+FN)

AUC计算方法总结

由 梦想与她 提交于 2019-12-20 04:34:44
一、roc曲线 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 rate FPR)特异度，划分实例中所有负例占所有负例的比例；(1-Specificity) 纵轴 ：真正类率(true postive rate TPR)灵敏度，Sensitivity(正类覆盖率) 2针对一个二分类问题，将实例分成正类(postive)或者负类(negative)。但是实际中分类时，会出现四种情况. (1)若一个实例是正类并且被预测为正类，即为真正类(True Postive TP) (2)若一个实例是正类，但是被预测成为负类，即为假负类(False Negative FN) (3)若一个实例是负类，但是被预测成为正类，即为假正类(False Postive FP) (4)若一个实例是负类，但是被预测成为负类，即为真负类(True Negative TN) TP :正确的肯定数目 FN :漏报，没有找到正确匹配的数目 FP :误报，没有的匹配不正确 TN :正确拒绝的非匹配数目 列联表如下，1代表正类，0代表负类： 由上表可得出横，纵轴的计算公式： (1)真正类率(True Postive Rate)TPR: TP/(TP+FN) ,代表分类器预测的

ROC曲线-阈值评价标准

由 微笑、不失礼 提交于 2019-12-20 04:34:12
　ROC曲线指受试者工作特征曲线 / 接收器操作特性曲线(receiver operating characteristic curve), 是反映 敏感性和特异性连续变量的综合指标 ,是用构图法揭示 敏感性和特异性的相互关系 ， 它通过将连续变量设定出多个不同的临界值，从而计算出一系列敏感性和特异性 ，再 以敏感性为纵坐标 、 （1-特异性）为横坐标 绘制成曲线， 曲线下面积越大，诊断准确性越高 。在ROC曲线上，最靠近坐标图 左上方的点 为敏感性和特异性均较高的临界值。 ROC曲线的例子 　　考虑一个二分问题，即将实例分成正类（positive）或负类（negative）。对一个二分问题来说，会出现四种情况。如果一个实例是正类并且也被 预测成正类，即为真正类（True positive）,如果实例是负类被预测成正类，称之为假正类（False positive）。相应地，如果实例是负类被预测成负类，称之为真负类（True negative）,正类被预测成负类则为假负类（false negative）。 TP：正确肯定的数目； FN： 漏报 ，没有正确找到的匹配的数目； FP： 误报 ，给出的匹配是不正确的； TN：正确拒绝的非匹配对数； 列联表如下表所示，1代表正类，0代表负类。 　　 预测 1 0 合计 实际 1 True Positive（TP） False Negative

机器学习和统计中的AUC

由 十年热恋 提交于 2019-12-20 01:08:19
首先，在试图弄懂AUC和ROC曲线之前，一定，一定要彻底理解混淆矩阵的定义！！！混淆矩阵中有着Positive、Negative、True、False的概念，其意义如下：称预测类别为1的为Positive（阳性），预测类别为0的为Negative（阴性）。预测正确的为True（真），预测错误的为False（伪）。对上述概念进行组合，就产生了如下的混淆矩阵： 然后，由此引出True Positive Rate（真阳率）、False Positive（伪阳率）两个概念： 仔细看这两个公式，发现其实TPRate就是TP除以TP所在的列，FPRate就是FP除以FP所在的列，二者意义如下： TPRate的意义是所有真实类别为1的样本中，预测类别为1的比例。 FPRate的意义是所有真实类别为0的样本中，预测类别为1的比例。 来源： CSDN 作者： 网络一线牵 珍惜这份缘 链接： https://blog.csdn.net/weixin_43838785/article/details/103608464

Sensitivity and specificity changes using a single threshold and a gradient of thresholds at 0.5 using pROC in R

由 旧时模样 提交于 2019-12-13 09:41:22
问题 I am trying to calculate ROC for a model of multi-class image. But since I didn't find any best way for multi-class classification, I have converted it to binary class. I have 31 classes of image. Using binary methods I am trying to find ROC of each 31 classes individually. df <- read.xlsx("data.xlsx",sheetName = 1,header = F) dn <- as.vector(df$X1) # 31 class model_info <- read.csv("all_new.csv",stringsAsFactors = F) # details of model output (Actual labels, Model labels, probabablity values
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