Which is more robust ROC or precision recall?
An ROC curve tends to be more robust to class imbalanace that a precision-recall curve. In this animation [below], both distributions start with 1000 outcomes. The blue one is then reduced to 50. The precision-recall curve changes shape more drastically than the ROC curve, and the AUC value mostly stays the same.
How are precision recall curves and ROC curves related?
The Precision-Recall AUC is just like the ROC AUC, in that it summarizes the curve with a range of threshold values as a single score. The score can then be used as a point of comparison between different models on a binary classification problem where a score of 1.0 represents a model with perfect skill.
How is AUC different from Roc and precision?
The precision-recall curve changes shape more drastically than the ROC curve, and the AUC value mostly stays the same. We also observe this behaviour when the other disribution is reduced to 50. Here’s the same, but now with the red distribution shrinking to just 50 samples.
How to calculate harmonic mean of precision and recall?
F-Measure or F1 score: that calculates the harmonic mean of the precision and recall ( harmonic mean because the precision and recall are rates). Area Under Curve: like the AUC, summarizes the integral or an approximation of the area under the precision-recall curve.
When is ROC AUC sensitive to class imbalance?
It depends how you mean the word sensitive. The ROC AUC is sensitive to class imbalance in the sense that when there is a minority class, you typically define this as the positive class and it will have a strong impact on the AUC value. This is very much desirable behaviour.
Is there any way to estimate the ROC-AUC for a random classifier?
If not, is there any way to estimate the ROC-AUC for a “random” classifier (fully blind to features and to the proportions of positives and negatives). What if we test against a stratified random classifier? (i.e. one that guesses positives and negatives randomly but according to the proportion of positives and negatives).
How to use ROC curves and precision-recall curves for classification?
Two diagnostic tools that help in the interpretation of probabilistic forecast for binary (two-class) classification predictive modeling problems are ROC Curves and Precision-Recall curves.