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It is given that:

MSE = bias$^2$ + variance

I can see the mathematical relationship between MSE, bias, and variance. However, how do we understand the mathematical intuition of bias and variance for classification problems (we can't have MSE for classification tasks)?

I would like some help with the intuition and in understanding the mathematical basis for bias and variance for classification problems.

Any formula or derivation would be helpful.

I don't fully understand the question, what are you looking for exactly? – Djib2011 – 2019-06-19T13:32:08.263

oops sorry. Updated in the question itself. What to know mathematical intuition of bias variance for classification problem. Fore regression it has relation with MSE but classification how to relate them.? – IamTheRealFord – 2019-06-21T08:51:44.597

WHAT classification? Logit? – Peter – 2019-06-21T20:01:06.417

If you are looking for the concept, see https://datascience.stackexchange.com/questions/53758/math-behind-mse-bias2-variance and deeplearningbook.

– Fatemeh Asgarinejad – 2019-06-25T06:43:41.3331ya already gone through that. But how will it work for classification problem.? (we dont have mse there know) – IamTheRealFord – 2019-06-25T07:17:25.747

I don't see why this was closed; the question seems pretty clear to me (after the June 25 edit anyway). MSE has a well-known bias-variance decomposition, so what about other (especially classification) losses? This doesn't depend on the specific model used. For a starting point, see https://stats.stackexchange.com/questions/393942/bias-variance-decomposition-for-non-squared-loss , but I haven't found a satisfactory answer for, e.g., log-loss.

– Ben Reiniger – 2019-06-29T13:57:32.623yes i am puzzled why my question is put on hold :( – IamTheRealFord – 2019-06-30T14:17:49.600