Optimal Hyperparameters
In machine learning, hyperparameters are parameters that are not learned from data but are set prior to training. These parameters can have a significant impact on the performance of a model. Optimal hyperparameters refer to the values of hyperparameters that result in the best performance of a model on a given task. Finding the optimal hyperparameters is often a challenging task and requires a combination of domain knowledge, experimentation, and optimization techniques. Hyperparameter tuning is the process of searching for the optimal hyperparameters for a given model and task. This can be done manually or using automated techniques such as grid search, random search, or Bayesian optimization. The optimal hyperparameters can vary depending on the dataset, the model architecture, and the task at hand.
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