In statistics and machine learning, the bias–variance tradeoff describes the relationship between a model's complexity, the accuracy of its predictions, and how well it can make predictions on previously unseen data that were not used to train the model. In general, as the number of tunable parameters in a model increases, it becomes more flexible, and can better fit a training data set.
In statistics and machine learning, the bias–variance tradeoff describes the relationship between a model's complexity, the accuracy of its predictions, and how well it can make predictions on previously unseen data that were not used to train the model. In general, as the number of tunable parameters in a model increases, it becomes more flexible, and can better fit a training data set.
Source: Wikipedia "Bias–variance tradeoff" · CC BY-SA 4.0
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