What is the bias-variance decomposition of classification error in ensemble methods?
A) It is a technique to analyze the performance of individual classifiers within an ensemble by decomposing the error into bias, variance, and noise components.
B) It is a method to evaluate the overall performance of an ensemble by measuring the trade-off between bias and variance, where bias represents the error due to overly simplistic assumptions and variance represents the error due to sensitivity to fluctuations in the training data.
C) It is an approach to optimize the hyperparameters of ensemble classifiers by decomposing the error into bias and variance terms and minimizing the sum of squared errors.
D) It is a process to assess the stability of ensemble classifiers by decomposing the error into bias and variance components and analyzing their respective contributions to the overall error.
