Linked Questions

0
votes
1answer
55 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 ...
1
vote
2answers
2k 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
39 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
42 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 ...
2
votes
2answers
107 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
325 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 ...
225
votes
5answers
333k 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.
1
vote
1answer
203 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 ...
5
votes
1answer
630 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 ...
2
votes
0answers
68 views

How to calculate the prediction score of a classificator?

I want to compare a given classification algorithm with others via the Area under the (ROC-)curve metric. Unfortunately this algorithm only outputs the values of the respective confusion matrix (TP, ...
0
votes
0answers
113 views

ROC curve interpretation [duplicate]

In the context of binary classification how do you interpret ROC curve: more precisely: 1) Why the diagonal stand for a random classifier? [Edit] Let's imagine a random classifier: each time he ...
0
votes
1answer
236 views

How are AUROC scores computed with just two vectors of actual and predicted values as input? [duplicate]

In the R package ModelMetrics, the auc score as shown in the documentation takes only two inputs; aucScore <- auc(actual=actuallabels, predicted=predictedlabels) where the inputs are pretty self ...
3
votes
1answer
2k views

draw roc curve on an example of 10 probability scores [duplicate]

I'm studying machine learning and find an example question on the book which really confused me. Q: A scoring classifier is evaluated on a test set of 10 examples resulting in the following ...
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 ...
1
vote
2answers
585 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 ...

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