I was told that you only run into severe problems with model accuracy if the base rates are far from 50%. Accuracy feels pretty interpretable and meaningful here as the base rates are 30%-50%.
It depends on how much signal there is in your data. If the base rate is 60%, but there’s so little signal in the data that the Bayes-optimal predictions only vary between 55% and 65%, then even a perfect model isn’t going to do any better than chance on accuracy. Meanwhile the perfect model will have a poor AUC but at least one that is significantly different from baseline.
[ROC AUC] penalises you for having poor prediction even when you set the sensitivity (the threshold) to a bad parameter. The F Score is pretty simple, and doesn’t have this drawback—it’s just a combination of some fixed sensitivity and specificity.
I’m not really sure what you mean by this. There’s no such thing as an objectively “bad parameter” for sensitivity (well, unless your ROC curve is non-convex); it depends on the relative cost of type I and type II errors.
The F score isn’t comparable to AUC since the F score is defined for binary classifiers and the ROC AUC is only really meaningful for probabilistic classifiers (or I guess non-probabilitstic score-based ones like uncalibrated SVMs). To get an F score for a binary classifier you have to pick a single threshold, which seems even worse to me than any supposed penalization for picking “bad sensitivities.”
there is ongoing research and discussion of this, which is confusing because as far as math goes, it doesn’t seem like that hard of a problem.
Because different utility functions can rank models differently, the problem “find a utility-function-independent model statistic that is good at ranking classifiers” is ill-posed. A lot of debates over model scoring statistics seem to cash out to debates over which statistics seem to produce model selection that works well robustly over common real-world utility functions.
It depends on how much signal there is in your data. If the base rate is 60%, but there’s so little signal in the data that the Bayes-optimal predictions only vary between 55% and 65%, then even a perfect model isn’t going to do any better than chance on accuracy.
Makes sense.
I’m not really sure what you mean by this. There’s no such thing as an objectively “bad parameter” for sensitivity (well, unless your ROC curve is non-convex); it depends on the relative cost of type I and type II errors.
I think they both have their strengths and weaknesses. When you give your model to a non-statistician to use, you’ll set a decision threshold. If the ROC curve is non-convex, then yes, some regions are strictly dominated by others. Then area under the curve is a broken metric because it gives some weight to completely useless areas. You could replace the dud areas with the bits that they’re dominated by, but that’s inelegant. If the second derivative is near zero, then AUC still cares too much about regions that will still only be used for an extreme utility function.
So in a way it’s better to take a balanced F1 score, and maximise it. Then, you’re ignoring the performance of the model at implausible decision thresholds. If you are implicitly using a very wrong utility function, then at least people can easily call you out on it.
For example, here the two models have similar AUC but for the range of decision thresholds that you would plausibly set the blue model, blue is better—at least it’s clearly good at something.
Obviously, ROC has its advantages too and may be better overall, I’m just pointing out a couple of overlooked strengths of the simpler metric.
Because different utility functions can rank models differently, the problem “find a utility-function-independent model statistic that is good at ranking classifiers” is ill-posed. A lot of debates over model scoring statistics seem to cash out to debates over which statistics seem to produce model selection that works well robustly over common real-world utility functions.
It depends on how much signal there is in your data. If the base rate is 60%, but there’s so little signal in the data that the Bayes-optimal predictions only vary between 55% and 65%, then even a perfect model isn’t going to do any better than chance on accuracy. Meanwhile the perfect model will have a poor AUC but at least one that is significantly different from baseline.
I’m not really sure what you mean by this. There’s no such thing as an objectively “bad parameter” for sensitivity (well, unless your ROC curve is non-convex); it depends on the relative cost of type I and type II errors.
The F score isn’t comparable to AUC since the F score is defined for binary classifiers and the ROC AUC is only really meaningful for probabilistic classifiers (or I guess non-probabilitstic score-based ones like uncalibrated SVMs). To get an F score for a binary classifier you have to pick a single threshold, which seems even worse to me than any supposed penalization for picking “bad sensitivities.”
Because different utility functions can rank models differently, the problem “find a utility-function-independent model statistic that is good at ranking classifiers” is ill-posed. A lot of debates over model scoring statistics seem to cash out to debates over which statistics seem to produce model selection that works well robustly over common real-world utility functions.
Makes sense.
I think they both have their strengths and weaknesses. When you give your model to a non-statistician to use, you’ll set a decision threshold. If the ROC curve is non-convex, then yes, some regions are strictly dominated by others. Then area under the curve is a broken metric because it gives some weight to completely useless areas. You could replace the dud areas with the bits that they’re dominated by, but that’s inelegant. If the second derivative is near zero, then AUC still cares too much about regions that will still only be used for an extreme utility function.
So in a way it’s better to take a balanced F1 score, and maximise it. Then, you’re ignoring the performance of the model at implausible decision thresholds. If you are implicitly using a very wrong utility function, then at least people can easily call you out on it.
For example, here the two models have similar AUC but for the range of decision thresholds that you would plausibly set the blue model, blue is better—at least it’s clearly good at something.
Obviously, ROC has its advantages too and may be better overall, I’m just pointing out a couple of overlooked strengths of the simpler metric.
Yes.