Answer Posted / Sumit Pratap Singh

Bias-variance decomposition decomposes the classification error into three components: bias, variance, and irreducible error. Bias refers to the error due to approximating a real-world relationship with an oversimplified model, variance refers to the error due to random fluctuations in the training data, and irreducible error refers to the inherent noise in the data.

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