Web Reference: Mar 2, 2019 · 0 Overfitting and underfitting are basically inadequate explanations of the data by an hypothesized model and can be seen as the model overexplaining or underexplaining the data. This is created by the relationship between the model used to explain the data and the model generating the data. Apr 28, 2021 · 68 Overfitting is likely to be worse than underfitting. The reason is that there is no real upper limit to the degradation of generalisation performance that can result from over-fitting, whereas there is for underfitting. Consider a non-linear regression model, such as a neural network or polynomial model. Sep 21, 2020 · I consider the issue of overfitting versus underfitting as related to the trade-off between bias and variance. Sure you can have situations that are both with high bias and high variance, but that is not the point of expressing the situation overfitting (relatively high variance) versus underfitting (relatively high bias).
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