What is bias-variance decomposition of classification error in ensemble method?

1 year ago
Machine Learning

The expected error of a learning algorithm can be decomposed into bias and variance. A bias term measures how closely the average classifier produced by the learning algorithm matches the target function. The variance term measures how much the learning algorithm’s prediction fluctuates for different training sets.

Sanisha Maharjan
Jan 10, 2022
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