roc

How to plot a ROC curve using ROCR package in r, with only a classification contingency table ? I have a contingency table where the true positive, false positive.. etc. all the rated can be computed. I have 500 replications, therefore 500 tables. But, I can not generate a prediction data indicating each single case of estimating probability and the truth. How can I get a curve without the individual data. Below is the package instruction used. ## computing a simple ROC curve (x-axis: fpr, y-axis: tpr) library(ROCR) data(ROCR.simple) pred <- prediction( ROCR.simple$predictions, ROCR.simple

I have fitted a SVM model and created the ROC curve with ROCR package. How can I compute the Area Under the Curve (AUC)? set.seed(1) tune.out=tune(svm ,Negative~.-Positive, data=trainSparse, kernel ="radial",ranges=list(cost=c(0.1,1,10,100,1000),gamma=c(0.5,1,2,3,4) )) summary(tune.out) best=tune.out$best.model ##prediction on the test set ypred = predict(best,testSparse, type = "class") table(testSparse$Negative,ypred) ###Roc curve yhat.opt = predict(best,testSparse,decision.values = TRUE) fitted.opt = attributes(yhat.opt)$decision.values rocplot(fitted.opt,testSparse ["Negative"], main =

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. Soren Havelund Welling 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 (changing aggregation rule). In randomForest this can be accessed with cutoff parameter

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 Confusion matrix: [[33229 1318] [ 1119 14334]] AUC= 0.944717975754 /////////////////////////////////////////

I know the AUC/ROC area ( http://weka.wikispaces.com/Area+under+the+curve ) in weka is based on the e Mann Whitney statistic ( http://en.wikipedia.org/wiki/Mann-Whitney_U ) But my doubt is, if I've got 10 labeled instances (Y or N, binary target attribute), by applying an algorithm (i.e. J48) onto the dataset, then there are 10 predicted labels on these 10 instances. Then what exactly should I use to calculate the AUC_Y, AUC_N, and AUC_Avg? Use the prediction's ranked label Y and N or the actual label (Y' and N')? Or I need to calculate the TP rate and FP rate? Can anyone give me a small

I am using ROCR package and i was wondering how can one plot a ROC curve for knn model in R? Is there any way to plot it all with this package? I don't know how to use the prediction function of ROCR for knn. Here's my example, i am using isolet dataset from UCI repository where i renamed the class attribute as y: cl<-factor(isolet_training$y) knn_isolet<-knn(isolet_training, isolet_testing, cl, k=2, prob=TRUE) Now my question is, what are the arguments to pass to the prediction function of ROC. I tried the 2 below alternatives which are not working: library(ROCR) pred_knn<-prediction(knn

I have a logistic regression model (using R) as fit6 <- glm(formula = survived ~ ascore + gini + failed, data=records, family = binomial) summary(fit6) I'm using pROC package to draw ROC curves and figure out AUC for 6 models fit1 through fit6. I have approached this way to plots one ROC. prob6=predict(fit6,type=c("response")) records$prob6 = prob6 g6 <- roc(survived~prob6, data=records) plot(g6) But is there a way I can combine the ROCs for all 6 curves in one plot and display the AUCs for all of them, and if possible the Confidence Intervals too. You can use the add = TRUE argument the plot

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, 0.8]) >>> fpr, tpr, thresholds = roc_curve(y, scores, pos_label=2) >>> fpr array([ 0. , 0.5, 0.5, 1. ])