The correct answer is: B. bias will be low, variance will be high
A degree 2 polynomial is a more complex model than a linear model. This means that it is able to fit the data more closely, but it is also more likely to overfit the data. Overfitting occurs when a model learns the noise in the data instead of the underlying pattern. This can lead to poor performance on new data.
A low bias means that the model is not systematically underestimating or overestimating the true value. A high variance means that the model is sensitive to small changes in the data. This can lead to the model making different predictions on different datasets, even if the datasets are very similar.
In general, it is desirable to have a model with low bias and low variance. However, it is often difficult to achieve both of these goals simultaneously. In some cases, it may be necessary to trade off between bias and variance. For example, if the data is very noisy, it may be necessary to use a more complex model (higher bias) in order to achieve low variance.
In the case of fitting a degree 2 polynomial, the model will have a lower bias than a linear model. This is because the polynomial model is able to fit the data more closely. However, the polynomial model will also have a higher variance than the linear model. This is because the polynomial model is more sensitive to small changes in the data.
Therefore, the correct answer is: B. bias will be low, variance will be high