Ruutuhaku

Ruutuhaku, also known as grid search, is a simple yet effective method for hyperparameter optimization in machine learning. It involves exhaustively searching through a manually specified subset of the hyperparameter space. The algorithm defines a grid of possible values for each hyperparameter and then trains and evaluates a model for every possible combination of these values.

The process begins by defining a set of hyperparameter values to explore. For example, if tuning a support vector machine, one might choose to test several values for the regularization parameter C and the kernel coefficient gamma. Ruutuhaku then systematically iterates through all possible pairs (or triplets, or more) of these predefined values. For each combination, a model is trained using the specified hyperparameters and evaluated on a validation set. The performance metric (e.g., accuracy, F1-score) is recorded for each combination.

After evaluating all combinations, the set of hyperparameters that yielded the best performance on the validation set is selected as the optimal configuration. While straightforward to implement and understand, ruutuhaku can become computationally expensive as the number of hyperparameters or the number of values to test for each hyperparameter increases. Its effectiveness is also limited by the quality of the manually chosen grid; important hyperparameter configurations might be missed if they fall between the tested grid points. Despite these limitations, ruutuhaku remains a popular choice for hyperparameter tuning, especially when the search space is relatively small or when a quick initial exploration is desired.