Why Precision and Recall Can Fool You — Meet Informedness and Markedness
We often trust scores like precision and recall, but these numbers can trick you.
They sometimes reward lucky guesses, or hide how much a system is really learning.
So a model that looks better by those counts might actually be worse, if you check the right way.
This piece points to simple ideas that show when a prediction is more than chance.
Informedness tells you how much a decision is actually useful, not only popular.
Its partner, markedness, looks from the other side — does the label strongly predict the outcome.
Together they give a clearer picture than single scores, and they tie into familiar ideas like ROC curves and correlation, but without fancy math.
Next time you see a performance report, pause.
Look for measures that account for bias and what would happen by chance.
Numbers can look great, but sometimes they simply don't mean what you think they do.
Read article comprehensive review in Paperium.net:
Evaluation: from precision, recall and F-measure to ROC, informedness,markedness and correlation
🤖 This analysis and review was primarily generated and structured by an AI . The content is provided for informational and quick-review purposes.
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