roc | 易学教程

Understanding ROC curve

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问题 import matplotlib.pyplot as plt from sklearn.metrics import roc_curve, auc , roc_auc_score import numpy as np correct_classification = np.array([0,1]) predicted_classification = np.array([1,1]) false_positive_rate, true_positive_rate, tresholds = roc_curve(correct_classification, predicted_classification) print(false_positive_rate) print(true_positive_rate) From https://en.wikipedia.org/wiki/Sensitivity_and_specificity : True positive: Sick people correctly identified as sick False positive:

Plot ROC curve from multiclass classifier with varying probability using scikit

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问题 The output of my multi-class classifier looks like this as shown below for which i need to plot ROC curve and get auc Utterence Actual Predicted Conf_intent1 Conf_Intent2 Conf_Intent3 Uttr 1 Intent1 Intent1 0.86 0.45 0.24 Uttr2 Intent3 Intent2 0.47 0.76 0.55 Uttr3 Intent1 Intent1 0.70 0.20 0.44 Uttr4 Intent3 Intent2 0.42 0.67 0.56 Uttr5 Intent1 Intent1 0.70 0.55 0.36 Note: Probability is done on absolute scoring so will not add to 1 for particular utterence the highest probability will be

ROC curve plot: 0.50 significant and cross-validation

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问题 I have got two problems of using pROC package to plot the ROC curve. A. The Significance level or P-value is the probability that the observed sample Area under the ROC curve is found when in fact, the true (population) Area under the ROC curve is 0.5 (null hypothesis: Area = 0.5). If P is small (P<0.05) then it can be concluded that the Area under the ROC curve is significantly different from 0.5 and that therefore there is evidence that the laboratory test does have an ability to

ROC curve using prebability of predicted class

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问题 I need to draw ROC curve using the predicted probabilities for two class problem. The need is to use different cutoff in probabilities to generate the ROC curve. I am predicting class probabilities using random forest mydata<-read.table(file="out-all-gm-pr-hpcuts-wor-noAl.tr", header=TRUE, sep ="") mydata$class <- as.factor(mydata$class) mydata.rf<-randomForest(class ~ ., data=mydata, importance = TRUE, mtry = 3, ntree = 100, proximity = TRUE ) Prediction on test data using above forest

Increase line width without stochastic bars ggplot

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问题 Does anyone know if it's possible to increase the line width in ggplot2 in a smooth fashion without adding random lines that stick out? Here's my original line plot and with size increased to 5: > ggplot(curve.df, aes(x=recall, y=precision, color=cutoff)) + > geom_line(size=1) Ideally, the final image would look something like the following plot from the PRROC Package, but I have another problem with plotting from there in that gridlines and ablines do not correspond to the axis tickmarks.

ROC curve and libsvm

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问题 Given a ROC curve drawn with plotroc.m (see here): Theoretical question: How to select the best threshold to be used? Programming qeuestion: How to induce the libsvm classifier to work with the selected (best) threshold? 回答1: ROC curve is plot generated by plotting fraction of true positive on y-axis versus fraction of false positive on x-axis. So, co-ordinates of any point (x,y) on ROC curve indicates FPR and TPR value at particular threshold. As shown in figure, we find the point (x,y) on

How to binarize RandomForest to plot a ROC in python?

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问题 I have 21 classes. I am using RandomForest. I want to plot a ROC curve, so I checked the example in scikit ROC with SVM The example uses SVM. SVM has parameters like: probability and decision_function_shape which RF does not. So how can I binarize RandomForest and plot a ROC? Thank you EDIT To create the fake data. So there are 20 features and 21 classes (3 samples for each class). df = pd.DataFrame(np.random.rand(63, 20)) label = np.arange(len(df)) // 3 + 1 df['label']=label df #TO TRAIN THE

Calculate Accuracy using ROCR Package in R

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问题 I am trying to calculate accuracy using ROCR package in R but the result is different than what I expected: Assume I have prediction of a model (p) and label (l) as following: p <- c(0.61, 0.36, 0.43, 0.14, 0.38, 0.24, 0.97, 0.89, 0.78, 0.86) l <- c(1, 1, 1, 0, 0, 1, 1, 1, 0, 1) And I am calculating accuracy of this prediction using following commands: library(ROCR) pred <- prediction(p, l) perf <- performance(pred, "acc") max(perf@y.values[[1]]) but the result is .8 which according to

ROC for random forest

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问题 I understand that ROC is drawn between tpr and fpr , but I am having difficulty in determining which parameters I should vary to get different tpr / fpr pairs. 回答1: I wrote this answer on a similar question. Basicly you can increase weighting on certain classes and/or downsample other classes and/or change vote aggregating rule. [[EDITED 13.15PM CEST 1st July 2015]] @ "the two classes are very balanced – Suryavansh" In such case your data is balanced you should mainly go with option 3

信息检索（IR）的评价指标介绍

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准确率、召回率、F1 信息检索、分类、识别、翻译等领域两个最基本指标是召回率(Recall Rate)和准确率(Precision Rate)，召回率也叫查全率，准确率也叫查准率，概念公式: 召回率(Recall) = 系统检索到的相关文件 / 系统所有相关的文件总数准确率(Precision) = 系统检索到的相关文件 / 系统所有检索到的文件总数图示表示如下：注意：准确率和召回率是互相影响的，理想情况下肯定是做到两者都高，但是一般情况下准确率高、召回率就低，召回率低、准确率高，当然如果两者都低，那是什么地方出问题了。一般情况，用不同的阀值，统计出一组不同阀值下的精确率和召回率，如下图：如果是做搜索，那就是保证召回的情况下提升准确率；如果做疾病监测、反垃圾，则是保准确率的条件下，提升召回。所以，在两者都要求高的情况下，可以用F1来衡量。 F1 = 2 * P * R / (P + R) 公式基本上就是这样，但是如何算图1中的A、B、C、D呢？这需要人工标注，人工标注数据需要较多时间且枯燥，如果仅仅是做实验可以用用现成的语料。当然，还有一个办法，找个一个比较成熟的算法作为基准，用该算法的结果作为样本来进行比照，这个方法也有点问题，如果有现成的很好的算法，就不用再研究了。 AP和mAP(mean Average Precision) mAP是为解决P，R，F