If a weight is estimated to be very close to zero, we can conclude that the corresponding attribute is not important and eliminate it from the model. These weights are the parameters of the model and are fine-tuned using data. The model is always fixed; it is the parameters that are adjustable, and it is this process of adjustment to better match the data that we call learning.
