roc | 易学教程

Plotting the ROC curve

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问题 If I have a matrix A of size m x n . The elements in the matrix represent the results of a specific detector. What I want is to characterize the performance of the detector by an ROC curve (sensitivity or Probability of detection by function of the probability of False alarm or 1-specificity). Interestingly, when (A(i,j) >= threshold) => the target is present , else it is absent . But of course, there will be some errors like as False alarm (False Positive) or Miss (False Negative). Lets

R fast AUC function for non-binary dependent variable

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问题 I'm trying to calculate the AUC for a large-ish data set and having trouble finding one that both handles values that aren't just 0's or 1's and works reasonably quickly. So far I've tried the ROCR package, but it only handles 0's and 1's and the pROC package will give me an answer but could take 5-10 minutes to calculate 1 million rows. As a note all of my values fall between 0 - 1 but are not necessarily 1 or 0. EDIT: both the answers and predictions fall between 0 - 1. Any suggestions?

How to fill in AUC of a ROC plot in R?

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问题 I have this ROC plot: library(Epi) library(pROC) data(aSAH) data <- aSAH plot.roc(data$outcome, data$s100b) I want to color the Area under the curve: 0.7314 . I tried variations on... x <- seq(1, 0, by = - .001) polygon(x, roc(data$outcome, data$s100b), col = rgb(.35,0.31,0.61, alpha = 0.4), border = rgb(.35,0.31,0.61, 0.4), lwd=2) ... and get the error message: Error in xy.coords(x, y) : 'x' and 'y' lengths differ . How can I make the lengths work out? 回答1: The plot.roc function from pROC

Scikit - How to define thresholds for plotting roc curve

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问题 I have a boosted trees model and probabilities and classification for test data set. I am trying to plot the roc_curve for the same. But I am unable to figure out how to define thresholds/alpha for roc curve in scikit learn. from sklearn.metrics import precision_recall_curve,roc_curve,auc, average_precision_score fpr = dict() tpr = dict() roc_auc = dict() fpr,tpr,_ = roc_curve(ytest,p_test, pos_label=1) roc_auc = auc(fpr,tpr) plt.figure() lw = 2 plt.plot(fpr, tpr, color='darkorange', lw=lw,

Plotting mean ROC curve for multiple ROC curves, R

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问题 I have a dataset of 100 samples, each of which has 195 mutations with their corresponding known clinical significance ("RealClass") and predicted value according to some prediction tool ("PredictionValues") For the demonstration, this is a random dataset that has the same structure as my dataset: predictions_100_samples<-as.data.frame(matrix(nrow=19500,ncol=3)) colnames(predictions_100_samples)<-c("Sample","PredictionValues","RealClass") predictions_100_samples$Sample<-rep(c(1:100), each =

How to interpret this triangular shape ROC AUC curve?

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问题 I have 10+ features and a dozen thousand of cases to train a logistic regression for classifying people's race. First example is French vs non-French, and second example is English vs non-English. The results are as follows: ////////////////////////////////////////////////////// 1= fr 0= non-fr Class count: 0 69109 1 30891 dtype: int64 Accuracy: 0.95126 Classification report: precision recall f1-score support 0 0.97 0.96 0.96 34547 1 0.92 0.93 0.92 15453 avg / total 0.95 0.95 0.95 50000

calculate cut-off that max sensitivity vs specificity using ROCR

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问题 I am trying to calculate the cut-off point that max sensitivity vs specifity. I am using the ROCR package and I have managed to plot the graph sensitivity vs specifity. However, I don't know how to calculate what is the cut off point that max sensitivity vs specifity. Ideal I would like to have a label in the graph that shows the cut off and the coordenates at the point. But, any suggestion to solve this question will be greatly appreciated. pred <- prediction( ROCR.simple$hello, ROCR.simple

thresholds in roc_curve in scikit learn

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问题 I am referring to the below link and sample, and post the plot diagram from this page where I am confused. My confusion is, there are only 4 threshold, but it seems the roc curve has many data points (> 4 data points), wondering how roc_curve working underlying to find more data points? http://scikit-learn.org/stable/modules/model_evaluation.html#roc-metrics >>> import numpy as np >>> from sklearn.metrics import roc_curve >>> y = np.array([1, 1, 2, 2]) >>> scores = np.array([0.1, 0.4, 0.35, 0

机器学习基础：ROC曲线与AUC计算详解

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AUC & ROC AUC是一个模型评价指标，只能用于二分类模型的评价，对于二分类模型，还有损失函数（logloss），正确率（accuracy），准确率（precision），但相比之下AUC和logloss要比accuracy和precision用的多，原因是因为很多的机器学习模型计算结果都是概率的形式，那么对于概率而言，我们就需要去设定一个阈值来判定分类，那么这个阈值的设定就会对我们的正确率和准确率造成一定成都的影响。二元分类算法，通过AUC(Area under the Curve of ROC(receiver operating characteristic))进行评估横坐标： 1-Specificity ，伪正类率(False positive rate， FPR)，预测为正但实际为负的样本占所有负例样本的比例；纵坐标： Sensitivity ，真正类率(True positive rate， TPR)，预测为正且实际为正的样本占所有正例样本的比例。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

Plot ROC curve from Cross-Validation (training) data in R

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问题 I would like to know if there is a way to plot the average ROC Curve from the cross-validation data of a SVM-RFE model generated with the caret package. My results are: Recursive feature selection Outer resampling method: Cross-Validated (10 fold, repeated 5 times) Resampling performance over subset size: Variables ROC Sens Spec Accuracy Kappa ROCSD SensSD SpecSD AccuracySD KappaSD Selected 1 0.6911 0.0000 1.0000 0.5900 0.0000 0.2186 0.0000 0.0000 0.0303 0.0000 2 0.7600 0.3700 0.8067 0.6280 0