If still not, an example of dataset where ROC AUC and AUPRC strongly disagrees would be great. An example would be most imbalanced datasets. PPV depends on the prevalence, so it would disagree with the TPR/FPR of the ROC curve …

2020年12月27日 · AUC (or AUROC, area under receiver operating characteristic) and AUPR (area under precision recall curve) are threshold-independent methods for evaluating a threshold-based classifier (i.e. logistic regression). Average precision score is a way to calculate AUPR. We'll discuss AUROC and AUPRC in the context of binary classification for simplicity.

2019年4月12日 · Both AUROC and AUPRC are overall measures. (AUPRC is simply average precision; see Menon and Williamson (2016), Lemma 52.) You care most about the precision (fraction of true-problem cases) in the top 1% (100 out of 10,000) of cases predicted to be problems, when the overall prevalence of problems is 5%. You thus might want to consider a ...

2018年11月14日 · I'm working in a very unbalanced classification problem, and I'm using AUPRC as metric in caret. I'm getting very differents results for the test set in AUPRC from caret and in AUPRC from package PRROC. In order to make it easy, the reproducible example uses PimaIndiansDiabetes dataset from package mlbench:

2024年5月24日 · It calculates the AUPRC using the mapped true labels and mapped predictions using the average_precision_score function. – Lucas F. T. Leonardo Commented May 25, 2024 at 21:11

2015年6月15日 · $\begingroup$ Maybe worth mentioning for future readers that the AP is not equal to the AUPRC for the scikit learn implementation, from the docs "This implementation is not interpolated and is different from computing the area under the precision-recall curve with the trapezoidal rule, which uses linear interpolation and can be too optimistic."

2021年6月24日 · delta == AUPRC - Baseline. AUPRC is an area under Precision-Recall curve. It’s a bit trickier to interpret AUPRC than it is to interpret AUROC (the area under the receiver operating characteristic). That’s because the baseline for AUROC is always going to be 0.5 — a random classifier, or a coin toss, will get you an AUROC of 0.5.

2022年5月21日 · I want to report the AUROC and the AUPRC of a prediction model using an unbalanced dataset. Is it correct that I have to balance my data to calculate the AUROC but leave the data unbalanced to calc...

AUPRC() is a function in the PerfMeas package which is much better than the pr.curve() function in PRROC package when the data is very large. pr.curve() is a nightmare and takes forever to finish when you have vectors with millions of entries. PerfMeas takes seconds in comparison. PRROC is written in R and PerfMeas is written in C.

2018年4月3日 · I'm also using other algorithms and to compare them I use the area under the precision-recall metric. The problem is the shape of the AUPRC for the DecisionTreeClassifier is a square and not the usual shape you would expect for this metric. Here is how I am calculating the AUPRC for the DecisionTreeClassifier.