The correct answer is A. The polynomial degree.
The polynomial degree is the number of terms in the polynomial regression model. A higher polynomial degree will result in a more complex model that can fit the data more closely, but it is also more likely to overfit the data. A lower polynomial degree will result in a simpler model that is less likely to overfit the data, but it may not be able to fit the data as closely.
The choice of polynomial degree is a trade-off between under-fitting and over-fitting. Under-fitting occurs when the model is too simple and does not fit the data well. Over-fitting occurs when the model is too complex and fits the data too closely, resulting in poor performance on new data.
The other options are not as important in terms of the trade-off between under-fitting and over-fitting. Whether we learn the weights by matrix inversion or gradient descent is a technical detail that does not have a significant impact on the model’s performance. The use of a constant-term is a common practice in regression modeling, but it does not have a major impact on the trade-off between under-fitting and over-fitting.