Advanced Quantitative Methods
The bias-variance tradeoff is a fundamental concept in statistical learning and machine learning that describes the balance between two types of errors that affect the performance of predictive models. Bias refers to the error due to overly simplistic assumptions in the learning algorithm, leading to systematic errors in predictions. Variance, on the other hand, refers to the error due to excessive complexity in the model, making it sensitive to fluctuations in the training data. Understanding this tradeoff is crucial for optimizing model performance, as too much bias can lead to underfitting while too much variance can lead to overfitting.
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