Signal Processing
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, which can lead to systematic underfitting, while variance refers to the error introduced by modeling the random noise in the training data, which can lead to overfitting. Understanding this tradeoff is crucial when working with signal denoising and compression, as it helps in selecting the right model complexity to achieve optimal performance.
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