What Is Hyperparameter optimization?
Hyperparameter optimization refers to the process of finding the optimal set of hyperparameters for a machine learning model or trading algorithm. Hyperparameters are external configuration settings that cannot be learned directly from the data and must be set before the training process begins.
Some common hyperparameter optimization techniques include grid search, random search, and Bayesian optimization. These methods aim to maximize the performance of the model or algorithm by systematically searching the hyperparameter space and evaluating different configurations using a predefined performance metric, such as accuracy or Sharpe ratio.
Hyperparameter optimization can help improve the performance of a model or trading strategy by fine-tuning its configuration to better fit the underlying data and problem.