A prior belief is an assumption held about data, parameters, or a model before observing the training evidence. In machine learning, prior beliefs can shape modeling choices, such as regularization methods that assume weights should remain small unless the data supports otherwise.
What you believe about the data before you begin training on it. For example, L~2~ regularization relies on a prior belief that weights should be small and normally distributed around zero.