Nov 11, 2023 · But, the ROC curve is often plotted, computed, based on varying the cutoff-value. (That's how I made the graph above, change the cutoff value and for each value compute false/true positive rates). Then, if you select a certain point on the ROC curve for the ideal cutoff, then you can just lookup which cutoff value/criterium created that point ...

Dec 12, 2015 · Here I put individual ROC curves as well as the mean curve and the confidence intervals. There are areas where curves agree, so we have less variance, and there are areas where they disagree. For repeated CV you can just repeat it multiple times and get the total average across all individual folds:

Dec 30, 2015 · A ROC Curve is not constructed for a set of data, it is constructed for the results of a classification performed on a set of data. There are models (or methods of implementing them) that produce multiple ROC curves for a single model and set- say, one for the results of the model applied to the training set itself and one for the results of ...

Jan 30, 2018 · You are showing individual ROC curves for each predictor, but I assume you have a multivariate model (e.g., logistic regression). What you should be showing is the ROC curve from the final model, using the predicted probability …

Feb 18, 2017 · ROC curves can sometimes be misleading in some very imbalanced applications. A ROC curve can still look pretty good (ie better than random) while misclassifying most or all of the minority class. In contrast, PR curves are specifically tailored for the detection of rare events and are pretty useful in those scenarios.

Now we see the problem with the ROC curve: for any search system, a recall (i.e. true positive rate) of 1 is reached for a very small false negative rate (before even 1% of negatives are misclassified as positive), and so the ROC curve (which plots recall against the false negative rate) almost immediately shoots up to 1.

Nov 8, 2014 · What does a point on ROC curve tells us, or if I have a ROC curve and I have taken a point like (0.4,0.8) (fpr,tpr) tells us? 3 Optimal classifier or optimal threshold for scoring

Jan 30, 2019 · However, comparing the ROC curves of the training set and the validation set can help. The size of the gap between the training and validation metrics is an indicator of overfitting when the gap is large, and indicates underfitting when there is no gap.

Mar 6, 2017 · In many other resources that I read, they calculated ROC curve on either training set or test set without a clear definition of "test set", so pardon me if I read it wrong. You want to calculate the ROC on the test set because that's actually the set of data that can help you estimate generalized performance, as it was not used to train the ...

Computing an ROC curve is done based on the ranking produced by your classifier (e.g. your logistic regression model). Use the model to predict every single test point once. You'll get a vector of confidence scores, let's call it $\mathbf{\hat{Y}}$. Using this vector you can produce the full ROC curve (or atleast an estimate thereof).