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Biasvariance trade-off is a key concept in machine learning optimization from "summary" of Introduction to Machine Learning with Python by Andreas C. Müller,Sarah Guido
Bias-variance trade-off is a key concept in machine learning optimization. The trade-off refers to the balance between the bias of the model and its variance. Bias is the error introduced by approximating a real-life problem, which may be complex, by a simpler model. On the other hand, variance refers to the amount that the estimate of the target function will change if different training data was used. In essence, bias is related to the model's assumptions about the data, while variance is related to the model's sensitivity to fluctuations in the training data. In machine learning, the goal is to find a model that accurately captures the underlying patterns in the data without overfitting or underfitting. Overfitting occurs when a model learns the training data too well, including noise and random fluctuations, which can lead to poor performance on new, unseen data. On the other hand, underfitting occurs when a model is too simple to capture the underlying pat...
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