roc | 易学教程

Plotting a linear discriminant analysis, classification tree and Naive Bayes Curve on a single ROC plot

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问题 The data is present at the very bottom of the page and is called LDA.scores'. This is a classification task where I performed three supervised machine learning classification techniques on the data-set. All coding is supplied to show how these ROC curves were produced. I apologise for asking a loaded question but I have been trying to solve these issues using different combinations of code for almost two weeks, so if anyone can help me, then thank you. The main issue is the Naive Bayes curve

ROC curve in R using rpart package?

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问题 I split Train data set and Test data set. I used a package rpart for CART (classification tree) in R (only train set). And I want to carry out a ROC analysis using the ROCR package. Variable is `n. use' (response varible... 1=yes, 0=no): > Pred2 = prediction(Pred.cart, Test$n.use) Error in prediction(Pred.cart, Test$n.use) : **Format of predictions is invalid.** This is my code. What is problem? And what is right type ( "class" or "prob" ? library(rpart) train.cart = rpart(n.use~., data=Train

How to compute AUC with ROCR package

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问题 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)

Plotting a linear discriminant analysis, classification tree and Naive Bayes Curve on a single ROC plot

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The data is present at the very bottom of the page and is called LDA.scores'. This is a classification task where I performed three supervised machine learning classification techniques on the data-set. All coding is supplied to show how these ROC curves were produced. I apologise for asking a loaded question but I have been trying to solve these issues using different combinations of code for almost two weeks, so if anyone can help me, then thank you. The main issue is the Naive Bayes curve shows a perfect score of 1, which is obviously wrong, and I cannot solve how to incorporate the linear

How to plot a ROC curve for a knn model

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问题 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

Plotting mean ROC curve for multiple ROC curves, R

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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$Sample<-rep(c(1:100), each = 195) predictions_100_samples$PredictionValues<-sample(seq(0,1,length.out=19500)) predictions_100_samples

Using cross validation and AUC-ROC for a logistic regression model in sklearn

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问题 I'm using the sklearn package to build a logistic regression model and then evaluate it. Specifically, I want to do so using cross validation, but can't figure out the right way to do so with the cross_val_score function. According to the documentation and some examples I saw, I need to pass the function the model, the features, the outcome, and a scoring method. However, the AUC doesn't need predictions, it needs probabilities, so it can try different threshold values and calculate the ROC

Scikit - How to define thresholds for plotting roc curve

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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, label='ROC curve (area = %0.2f)' % roc_auc) plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--'

ROC curve in R using rpart package?

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I split Train data set and Test data set. I used a package rpart for CART (classification tree) in R (only train set). And I want to carry out a ROC analysis using the ROCR package. Variable is `n. use' (response varible... 1=yes, 0=no): > Pred2 = prediction(Pred.cart, Test$n.use) Error in prediction(Pred.cart, Test$n.use) : **Format of predictions is invalid.** This is my code. What is problem? And what is right type ( "class" or "prob" ? library(rpart) train.cart = rpart(n.use~., data=Train, method="class") Pred.cart = predict(train.cart, newdata = Test, type = "class") Pred2 = prediction

Feature selection + cross-validation, but how to make ROC-curves in R

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问题 I'm stuck with the next problem. I divide my data into 10 folds. Each time, I use 1 fold as test set and the other 9 as training set (I do this ten times). On each training set, I do feature selection (filter methode with chi.squared) and then I make a SVMmodel with my training set and the selected features. So at the end, I become 10 different models (because of the feature selection). But now I want to make a ROC-curve in R from this filter methode in general. How can I do this? Silke 回答1: