Linked Questions

2
votes
1answer
41 views

Area under the curve score training/validation set

Lets say I have a very basic, binary classification problem and I use logistic regression. The logistic regression will give me a score (not a classification yet), between 0 and 1. I can use sklearn'...
248
votes
6answers
380k views

What does AUC stand for and what is it?

Searched high and low and have not been able to find out what AUC, as in related to prediction, stands for or means.
0
votes
0answers
8 views

How to understand the mathematics of ROC/AUC in Wikipedia [duplicate]

Could someone help explain what happened in the red box? I'm struggling to figure out where the indicator function comes from in this calculation. Thank you~ wiki link
0
votes
1answer
50 views

How to “draw” a ROC curve [duplicate]

I have read this question but it doesn't have any well-explained answer for this case: I understand the ROC curve in overall, but I'm looking for a step-to-step explanation in order to understand how ...
1
vote
1answer
50 views

AUC ROC and probabilistic interpretation

I can't solve the problem about the AUC ROC metric. Problem condition: on the answers (estimates) of the algorithm, objects of class 0 are distributed uniformly on the segment [0, 2/3], and answers of ...
0
votes
0answers
8 views

What is threshold in ROC curve? [duplicate]

Whenever I read about ROC, people say that it is graphical representation of True Positive Rate value and False Positive Rate at various threshold. Whenever I read in detail, people explain that ...
2
votes
3answers
909 views

AUC with incomplete ROC curve

I am doing an experiments where changing a parameter I am obtaining different number of FalsePositive, FalseNegative... and so on. I am using this parameter tuning as threshold tuning to obtain FPR ...
1
vote
1answer
814 views

Cox regression with lasso regression

Is it possible to perform lasso regression (glmnet with "cox") for variable selection and then conduct Cox regression using ...
2
votes
2answers
4k views

Meaning of model calibration

A previous post has discussed model discrimination very nicely. The post also briefly discussed calibration: "When evaluating a risk model, calibration is also very important. To examine this, ...
0
votes
1answer
57 views

Correct conditional expectation via logistic regression but terrible AUC

Suppose you have a binary random variable $Y$, and several other random variables $X_1,...,X_p$. Your goal is to "predict $Y$ using $X_1,...,X_p$." So, you go ahead and fit logistic regression, which ...
0
votes
0answers
108 views

How to interpret ROC curve? [duplicate]

I am currently doing a classification problem for classifying the functional class and non-functional class of peptidase cleavage site. The data on non-functional class (negative class) is highly ...
4
votes
2answers
353 views

Why does pROC roc work with non-probability predictions?

With the pROC package, I can do this: true <- c(1, 1, 1, 0) predicted <- c(0.5, 0.1, 0.6, 0.1) roc(true, predicted) which gives as expected: ...
3
votes
1answer
415 views

Different visualization of AUC than ROC curve

There are multiple interpretations of area under ROC curve. (e.g What does AUC stand for and what is it? ). We also know that AUC is closely related to rank correlation. Are there also different ways ...
3
votes
1answer
363 views

Distributed AUC calculation (or approximation)

I am trying to calculate the ROC AUC for a dataset where I can't fit predictions and labels in memory (10s/100s billions of samples). Is there a way to calculate the AUC in a distributed way or at ...
6
votes
1answer
1k views

How is a ROCAUC=1.0 possible with imperfect accuracy? [duplicate]

I used sklearn to compute roc_auc_score for a dataset of 72 instances. The accuracy was at 97% (2 misclassifications), but the ROC AUC score was 1.0. How is this ...

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