Developing an effective and accurate ML model to solve a problem is one of the goals of any AI project. To optimize the model, we need to tune its parameters and hyperparameters and then evaluate whether the updates result in the anticipated improvements. This requires setting up key metrics and defining a model evaluation procedure. After implementing the changes and conducting evaluation, we can determine if the performance of the ML model has improved and whether we should use the updated version.
What are hyperparameters and how do they differ from parameters?
Parameters in machine learning are special coefficients or weights of the model that are selected and tuned during the training process. They are estimated by fitting the training data to the model and are subsequently used to make predictions on new data.
In contrast, hyperparameters are set before the training process. They are unique settings that govern the learning process in ML and directly impact the model parameters that an algorithm learns.
Hyperparameters can’t alter themselves while the model is in its learning or training stage, and they don’t make it into the final model. Moreover, it’s impossible to discern the values of the hyperparameters used during training just…
