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 ...

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.

sklearn.metrics.roc_curve() 函数是用于计算二分类问题中的接收者操作特征曲线（ROC 曲线）以及对应的阈值。ROC 曲线是以假阳性率（False Positive Rate, FPR）为横轴，真阳性率（True Positive Rate, TPR）为纵轴，绘制的分类器性能曲线。

Nov 30, 2014 · The area under the ROC-curve is a measure of the total discriminative performance of a two-class classifier, for any given prior probability distribution. Note that a specific classifier can perform really well in one part of the ROC-curve but show a poor discriminative ability in a different part of the ROC-curve.

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

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).

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.

If sensitivity and specificity have the same importance to you, one way of calculating the cut-off is choosing that value that minimizes the Euclidean distance between your ROC curve and the upper left corner of your graph. Another way is using the value that maximizes (sensitivity + specificity - 1) as a cut-off.

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:

I used the functions from this link for creating ROC curve for logistic regression model. Since the object produced by glmer in lme4 package is a S4 object (as far as I know) and the function from the link cannot handle it. I wonder if there are similar functions for creating ROC curve for multi-level logistic regression model in R.