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Hyper-parameters are parameters used to regulate how an algorithm behaves while it creates a model. Hyperparameter optimization (HPO) is the process of finding the optimum combination of hyper-parameters that produce the greatest performance. There are several automated optimization methods, each with advantages and disadvantages depending on the task. The top hyperparameter optimization libraries and tools for ML models are BayesianOptimisation, GPyOpt, Hyperopt, and Keras Tuner.
