Biostatistics
The bias-variance tradeoff is a fundamental concept in machine learning and statistics that describes the balance between two types of errors that affect model performance: bias and variance. Bias refers to the error introduced by approximating a real-world problem with a simplified model, while variance refers to the error caused by excessive sensitivity to small fluctuations in the training data. A good model should achieve a balance where both bias and variance are minimized, leading to improved predictive accuracy.
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