Parameters and Loss function

In machine learning, a parameter refers to a value that the model learns from the training data during the learning process. These parameters are adjusted and optimized to minimize the error or loss in the model’s predictions.

Example: Let’s consider the waiting times for a bus to arrive, which is uniform between 1 and 10 minutes. The parameters and define this range. Every time you sample from this distribution, the waiting time will be uniformly spread between 1 and 10 minutes.

In summary, and are the parameters of the uniform distribution that define the boundaries of the values the model can generate.

In a nutshell, parameters are the knobs the model adjusts to best fit the data and make accurate predictions!