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Hyperparameter: Difference between revisions
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Selecting the optimal set of hyperparameters for machine learning models is a vital task, often involving hyperparameter optimization. This involves searching for the optimal set of hyperparameters that produces the best performance from a model. There are various methods available for optimizing hyperparameters, such as: | Selecting the optimal set of hyperparameters for machine learning models is a vital task, often involving hyperparameter optimization. This involves searching for the optimal set of hyperparameters that produces the best performance from a model. There are various methods available for optimizing hyperparameters, such as: | ||
*Grid | *[[Grid search]]: This method involves evaluating a model's performance against a grid of hyperparameter values. While this can be time-consuming for models with many parameters, it is simple to implement and usually produces promising outcomes. | ||
*Random | *[[Random search]]: This technique involves randomly selecting hyperparameters from a distribution and evaluating the model's performance for each set. While it may be faster than grid search in models with many hyperparameters, it does not guarantee finding the optimal set of hyperparameters. | ||
*Bayesian | *[[Bayesian optimization]]: This technique involves creating a probabilistic model of the hyperparameters and using it to select the most promising set for evaluation. It can be more efficient than grid search or random search, often producing good results with fewer evaluations. | ||
==Explain Like I'm 5 (ELI5)== | ==Explain Like I'm 5 (ELI5)== | ||