Low priority feature request: support for multi-class roc_auc score calculation in sklearn.metrics using the one against all methodology would be incredibly useful.
(In the context of machine learning applications, X and Y represent the confidence score, i.e., the predicted probability of the positive class, generated by a ML algorithm.) For any threshold c, a subject is classified as a positive case if its test result (confidence score) is larger ...
A perfect classifier would have an AUC-ROC score of 1, meaning that it can perfectly distinguish between positive and negative classes. On the other hand, a random classifier would have an AUC-ROC score of 0.5, meaning that it cannot distinguish between positive and negative classes. Conclusion...
As above, assume that we are looking at a dataset where we want to distinguish data points oftype 0from those oftype 1. Consider a classification algorithm that assigns to a random observation a score (or probability) signifying membership inclass 1. If the final classification betweenclass 1an...
简单翻译下:首先AUC值是一个概率值,当你随机挑选一个正样本以及一个负样本,当前的分类算法根据计算得到的Score值将这个正样本排在负样本前面的概率就是AUC值。AUC值越大,当前的分类算法越有可能将正样本排在负样本前面,即能够更好的分类。另外,AUC与Gini分数有联系,Gini + 1 = 2*AUC。
Image credit: Wikipedia From a random classifier you can expect as many true positives as false positives. That’s the dashed line on the plot. AUC score for the case is 0.5. A score for a perfect classifier would be 1. Most often you get something in between. ...
— F-1 score = (2*Precision*Recall)/(Precision+Recall)=(2*0.5*1)/(0.5+1) =2/3. D2 at0.75threshold, F1 score(=2/3). Now let’s do something interesting. Let’s look at the AUCs. Original image For D1, as you go from threshold 0 to 0.5, the TPR remains constant (=1)....
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metrics import roc_auc_score, roc_curve import matplotlib.pyplot as plt # load the dataset data = load_breast_cancer() X = data.data y = data.target # split the data into train and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_...
The calculation of the area under this curve (the AUC score) provides a single-number discrimination measure across all possible ranges of thresholds. This discrimination measure is equivalent to the non-parametric Wilcoxon test (Hanley & McNeil, 1982), in which the rank of all possible pairs ...