Statistical Methods for Data Science
The bias-variance tradeoff is a fundamental concept in statistics and machine learning that describes the balance between two types of error in predictive models: bias, which refers to the error due to overly simplistic assumptions in the learning algorithm, and variance, which refers to the error due to excessive complexity in the model. Understanding this tradeoff is crucial when creating models that aim for good predictive performance while avoiding overfitting or underfitting.
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