hyperparameter Tuning
时间: 2023-12-11 19:03:34 浏览: 28
Hyperparameter tuning is the process of selecting the optimal set of hyperparameters for a machine learning model in order to achieve the best possible performance. Hyperparameters are variables that are set before training a model and cannot be learned from the data. Examples of hyperparameters include learning rate, batch size, number of layers, and regularization strength.
Hyperparameter tuning involves exploring different combinations of these hyperparameters and evaluating their performance on a validation set. This process can be done manually by trying different values for each hyperparameter, or through automated methods such as grid search, random search, or Bayesian optimization.
The goal of hyperparameter tuning is to find the set of hyperparameters that results in the best performance on the validation set. This can help to avoid overfitting or underfitting, and improve the generalization of the model to new data.