The bias-variance trade-off is a fundamental concept in machine learning that refers to the relationship between a model's ability to fit the training data and its ability to generalize to new, unseen data.
Bias refers to the difference between the expected or average prediction of a model and the true value of the target variable. High bias means that the model is too simple and fails to capture the underlying patterns in the data, leading to underfitting.
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Variance, on the other hand, refers to the amount by which the model's predictions vary for different training datasets. High variance means that the model is too complex and overfits the training data, capturing the noise and randomness in the data, and performing poorly on new data.
The trade-off between bias and variance occurs because reducing one typically increases the other. Finding an optimal balance between bias and variance is crucial to building a model that performs well on both the training and testing data. This can be achieved through various techniques such as regularization, cross-validation, and ensembling.
