Answer Posted / Vinay Kumar Dubey

The bias-variance decomposition of classification error in an ensemble method breaks down the total error into three components: bias squared, variance, and irreducible error. The bias represents the error due to modeling assumptions, variance is the error due to training on a noisy dataset, and the irreducible error is the minimum error achievable by any model of that complexity.

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