Information Theory
The bias-variance tradeoff is a fundamental concept in statistical learning that describes the balance between two sources of error in predictive models: bias, which represents the error due to overly simplistic assumptions in the learning algorithm, and variance, which captures the error due to excessive sensitivity to fluctuations in the training data. Understanding this tradeoff is crucial for optimizing model performance, as minimizing one source of error can lead to an increase in the other.
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