What is model tuning?
Model tuning is the process of determining the parameters of the model that lead to the best results. Model tuning usually involves an iterative trial-and-error process, with minor modifications in the hyperparameters or data. The model is trained multiple times with all possible setups, and its performance is compared to other structures.
Why is it important?
Model tuning allows us to increase the model’s accuracy by picking the parameters that best suit the problem we are solving.