What Are Hyperparameters And How Do They Determine A Model's Performance - Analytics India Magazine

A key balancing act in machine learning is choosing an appropriate level of model complexity: if the model is too complex, it will fit the data used to construct the model very well but generalise poorly to unseen data (overfitting); if the complexity is too low the model won't capture all the information in the data (underfitting). In deep learning or machine learning scenarios, model performance depends heavily on the hyperparameter values selected. The goal of hyperparameter exploration is to search across various hyperparameter configurations to find a configuration that results in the best performance. Typically, the hyperparameter exploration process is painstakingly manual, given that the search space is vast and evaluation of each configuration can be expensive. The model parameters tell how to transform input data into desired output whereas, the hyperparameters are used to determine the structure of the model in use.