bias variance decomposition for classification problem

Question

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.

Eugen · Answer

My opinion is that the bias variance trade off is rooted in the Uncertainty principle. It behaves absolutely the same.

ombk · Answer

Bias and Variance in Classification problems
Check this link about Support Vector Machine.
You will directly understand bias and variance in classification. Basically, if your data is linearly separable you do not have a problem.
But imagine that your data is pseudo/semi linearly separable, however, few points land on the other side of their group.
Now imagine having a model that separates the data linearly, vs a model that will oscillate through the data so much to be able to classify correctly every point.

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bias variance decomposition for classification problem

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