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Gere1 t1_itbcbfq wrote

Machinelearningmastery is rather shallow, but he tries to spread and monetize it aggressively. And searches will pick up on this.

But if you believe P-R-curves are bad for imbalanced data, then you are just as mistaken. For example, precision and recall is exactly what you need for fraud detection. ML isn't about opinions and hand-wavy reasoning about math, but about getting results which work in the real world.

Now, you are asking for blogs specifically. What type information would you like to see? Learning the basics like ROC curves so probably better done from books or practice instead of waiting for blog posts. For more research level information there are many blogs, but it depends on the field (CV, NLP, ...). For a regular overview over what's happening you could look into https://jack-clark.net/ or https://datamachina.substack.com/

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likeamanyfacedgod OP t1_ithsgdh wrote

Thanks! FYI, I believe that PR curves are good for imbalanced data. My beef is that I also believe that ROC curves are good for imbalanced data.

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Gere1 t1_ithw25k wrote

I agree. If anything, then ROC curves have some "academic" reasons to be rather good than bad for imbalanced data.

I think there are a lot of low quality data science blog posts out there. In the end only something with measurable success (like an ML competition winner) indicates something worth looking into.

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