A fixed classifier is a machine learning model chosen independently of the training data. While such a classifier generalizes perfectly—meaning its empirical error on a new dataset serves as an unbiased estimate of its population error—it is practically useless because it fits neither the training data nor the underlying population distribution well. It represents the extreme rigid (high bias) end of the trade-off between model flexibility and rigidity.