Hyperparameter tuning is the process of finding the optimal set of hyperparameters for a machine learning model. Hyperparameters are external configuration variables that data scientists use to manage machine learning model training. They are different from parameters, which are internal parameters automatically derived during the learning process and not set by data scientists. Hyperparameters directly control model structure, function, and performance.
When training machine learning models, each dataset and model needs a different set of hyperparameters, which are a kind of variable. The only way to determine the best set of hyperparameters is through hyperparameter tuning. Hyperparameter tuning allows data scientists to tweak model performance for optimal results. The process of hyperparameter tuning is iterative, and different combinations of parameters and values are tried out.
Hyperparameter tuning can be done either manually or by automated methods. Manual search is a method of hyperparameter tuning in which the data scientist or machine learning engineer manually selects and adjusts the hyperparameters of the model. Automated hyperparameter tuning methods use an algorithm to search for the optimal values. Some of the most popular automated methods are grid search, random search, and Bayesian optimization.
Hyperparameters might address model design questions such as the degree of polynomial features to use for a linear model, the maximum depth allowed for a decision tree, the minimum number of samples required at a leaf node in a decision tree, the number of trees to include in a random forest, the number of neurons to have in a neural network layer, and the number of layers to have in a neural network.
In summary, hyperparameter tuning is the process of finding the optimal set of hyperparameters for a machine learning model. It is an essential part of machine learning, and choosing appropriate hyperparameter values is crucial for success.
