Importance of Hyper-parameters in Model development

Machine Learning (ML) development is an iterative process in which the accuracy of predictions made by the models is continuously improved by repeating the training and evaluation phases. In each of these iterations, certain parameters are tweaked continuously by developers. Any parameter manually selected based on learning from previous experiments qualify to be called a model hyper-parameter. These parameters represent intuitive decisions whose value cannot be estimated from data or from ML theory. The hyper-parameters are knobs that you tweak during each iteration of training a model to improve the accuracy in the predictions made by the model. The hyper-parameters are variables that govern the training process itself.