hiperparaméterteret
hiperparaméterteret refers to the set of possible values for the hyperparameters of a machine learning model. Hyperparameters are parameters whose values are set before the learning process begins. They are not learned from the data itself but are instead chosen by the machine learning practitioner. Examples of hyperparameters include the learning rate in gradient descent, the number of trees in a random forest, or the C and gamma parameters in a support vector machine.
The hiperparaméterteret defines the search space for these hyperparameters. When tuning hyperparameters, the goal is to
The size and structure of the hiperparaméterteret significantly impact the efficiency and effectiveness of hyperparameter tuning.