Linked Questions

-1
votes
1answer
803 views

ROC AUC score significance [duplicate]

I used the sklearn.metric roc_auc_score,it gave me a value 0.91. What is the does this number mean? I am interested to learn how this is calculated,could someone ...
2
votes
0answers
296 views

does the area under the curve (AUC) has some interpretation? [duplicate]

I know that the ROC curve plots true positives vs false positives, but does it have any other interpretation, or is it just an arbitrary performance measure? Also, in the case of very unbalanced ...
75
votes
5answers
110k views

How to calculate Area Under the Curve (AUC), or the c-statistic, by hand

I am interested in calculating area under the curve (AUC), or the c-statistic, by hand for a binary logistic regression model. For example, in the validation dataset, I have the true value for the ...
57
votes
1answer
56k views

Understanding ROC curve

I'm having trouble understanding the ROC curve. Is there any advantage / improvement in area under the ROC curve if I build different models from each unique subset of the training set and use it to ...
11
votes
3answers
5k views

How to derive the probabilistic interpretation of the AUC?

Why is the area under the ROC curve the probability that a classifier will rank a randomly chosen "positive" instance (from the retrieved predictions) higher than a randomly chosen "positive" one (...
12
votes
1answer
2k views

Why is ROC AUC equivalent to the probability that two randomly-selected samples are correctly ranked? [duplicate]

I found there are two ways to understand what AUC stands for but I couldn't get why these two interpretations are equivalent mathematically. In the first interpretation, AUC is the area under the ...
4
votes
1answer
1k views

Is the F-1 score symmetric?

Below is the report of my out-of-bag precision, recall and f-1score when using ...
0
votes
2answers
2k views

what it means to have low ROC AUC?

I have trained two classifiers namely a logistic regression and a decision tree on a data set. When evaluating both models on the testing data set, the decision tree has a ROC AUC = 0.62 but the ...
5
votes
1answer
2k views

How to compute the AUROC for a single categorical variable

I am building new features for a binary classifier. The new features fall into two categories: categorical and ordinal. An example of the first feature would be the colours ...
4
votes
3answers
631 views

Averaging model Coefficients in binary logistic regression

I am running a binary logistic regression to estimate the probability for frost occurrences of a frost event given a set of explanatory variables. I intent to carry an accuracy assessment and ...
2
votes
1answer
275 views

Is there any effect of unbalanced dataset on AUROC?

I have worked on many classification problems. One of the parameters for classifier performance is AUROC/AUC which is the area under curve created by TPR and FPR values for different cutoffs of ...
1
vote
1answer
1k views

Skewed dataset performance measurement in machine learning

Let's say we're building a spam classifier. When we feed it an email, it accurately classifies it as spam/not-spam 98% of the time. Then we discover that 99% of the email we receive is actually spam. ...
0
votes
1answer
883 views

What do the thresholds on x and y axis of ROC curve represent?

There is a detailed explanation of what the AUC of an ROC curve is here. However I have searched high and low for an explanation regarding what the X and y axes of the ROC curve are. I have understood ...
2
votes
2answers
656 views

AUROC equal to 1.0 means overfitting?

Evaluating the classifier I implemented for university, I am observing an AUROC (Area under curve of the ROC) of 1.0 (which means a TP rate of 1 and a FP rate of 0.0) The dataset used for training ...
5
votes
3answers
300 views

Correctly expressing improvement in AUC?

If a set of features, A, results in an AUC of 0.5, then an improved set of features, B, were used resulting in an AUC of 0.75, how do I express this improvement in words: 50% improvement (0.75-0.5)?...

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