Soundness of mind

Can you have high bias and high variance?

Yes, it is possible to have both high bias and high variance. This is called the «double descent» phenomenon, which occurs when a model has both high bias and high variance. High bias means the model is underfitting the data, while high variance means the model is overfitting the data. In both cases, the model is unable to make accurate predictions, resulting in low accuracy. To address this issue, it’s important to find a balance between the two by tuning the model’s hyperparameters.

Can there be high bias and high variance?

Yes, it is possible for a model to have both high bias and high variance. High bias occurs when the model is overly simplified and does not capture all of the complexities of the data, resulting in poor accuracy. High variance occurs when the model is overly complex and has too many parameters that can easily be tweaked, resulting in overfitting. In order to get the best results, it is important to find a balance between underfitting and overfitting, so that the model is neither too simple nor too complex.

What does high variance and high bias mean?

High variance and high bias are terms commonly used to describe machine learning models. High variance means that a model is able to accurately capture the underlying structure of the data it is trained on, but has difficulty generalizing to new data. High bias, on the other hand, means that a model is unable to accurately capture the underlying structure of the data it is trained on, and therefore has difficulty generalizing to new data. High variance and high bias can be caused by overfitting or underfitting the data. To reduce these issues, regularization techniques such as L2 regularization and dropout can be used to help improve the model’s performance.