Hyperparameters
Machine/deep learning models consist of two types of parameters: model parameters and hyperparameters. Hyperparameters are external configuration variables set by us to operate machine model training. They are parameters that define the details of learning process. Examples of hyperparameters include number of nodes and layers in neural networks, learning rates, epochs etc. They have major impact on the accuracy and efficiency of the training model and hence they need to be defined in such a way so as to get the best results. This leads us to the topic of hyperparameter optimization.
Hyperparameter Optimization Based on Bayesian Optimization
In this article we explore what is hyperparameter optimization and how can we use Bayesian Optimization to tune hyperparameters in various machine learning models to obtain better prediction accuracy. Before we dive into the how’s of implementing Bayesian Optimization, let us learn what is meant by hyperparameters and hyperparameter optimization.