Hyperparameter tuning finetunes model performance from "summary" of Machine Learning by Ethem Alpaydin
Hyperparameter tuning is a critical step in the machine learning process. It involves adjusting the parameters of a model that are not learned during training, such as the learning rate in a neural network. Tuning these hyperparameters is essential for maximizing the performance of a machine learning model. By carefully selecting the values of hyperparameters, we can achieve better results in terms of accuracy, speed, and generalization. The process of hyperparameter tuning is often iterative, involving multiple rounds of training and evaluation to find the optimal combination of hyperparameters for a given dataset. Hyperparameter tuning can be done manually, by trying different values and evaluating the results, or automatically, using techniques such as grid search or random search. Automated hyperparameter tuning algorithms can explore the hyperparameter space more efficiently and effectively than manual tuning. In practice, hyperparameter tuning can make a significant difference in the performance of a machine learning model. Even small changes in hyperparameters can lead to substantial improvements in accuracy and generalization. Therefore, it is crucial to spend time and effort on tuning hyperparameters to achieve the best possible results.
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