- #1
fog37
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- TL;DR Summary
- Classifiers, threshold, and ROC curve
Hello,
A classifier is a ML model that can classify between 2 or more classes. Some classifiers are called probabilistic in the sense that they output a probability score that is then compared against a threshold value (usually 0.5) to make the class decision. Other classifiers are not probabilistic...I guess they are called deterministic. We can always plot the ROC curve for a binary classifier. The ROC curve depends on TPR, FPR and various explored threshold values. The TPR and FPR vary for different threshold values...
Do all deterministic classifiers make their decision also based on some set threshold? If so, does it mean that we can plot the ROC curve for any classifier, probabilistic or not?
Thank you!
A classifier is a ML model that can classify between 2 or more classes. Some classifiers are called probabilistic in the sense that they output a probability score that is then compared against a threshold value (usually 0.5) to make the class decision. Other classifiers are not probabilistic...I guess they are called deterministic. We can always plot the ROC curve for a binary classifier. The ROC curve depends on TPR, FPR and various explored threshold values. The TPR and FPR vary for different threshold values...
Do all deterministic classifiers make their decision also based on some set threshold? If so, does it mean that we can plot the ROC curve for any classifier, probabilistic or not?
Thank you!