Use AUC to evaluate multiclass problems

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AUC is an excellent evaluation metric for binary classification, especially if you have class imbalance.
New in scikit-learn 0.22: AUC can be used with multiclass problems! Supports "one-vs-one" and "one-vs-rest" strategies.

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It would be nice to hear about your thoughts on AUC ROC vs AUC PR, for imbalanced sets (or any other metic for that matter). Thanks for sharing!

cgmiguel

thank you for all your vids, really appreciated.

do you plan to do a short series for plotting? would love to see this on your channel if you find the time to do so at one point.

either way, thanks a lot. you are such a great teacher!

felixisnr

So how to decide which one should i use actually ovo or ovr if i have 4 classes to be classified

beautyisinmind

Really Helpful 🙂.Can you please suggest a demo dataset for multi class classification

priyabrataray

nice, good explanation .
I have question can we do binomial /poisson test experiment on a dataset?

svitirur

axis 1 is out of bounds for array of dimension 1

debabratasikder

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