Three Essential Hyperparameter Tuning Techniques for Better Machine Learning Models

Hyperparameter tuning is the iterative process of optimizing a machine learning model's performance by finding the best values for its hyperparameters without causing overfitting. It is important to distinguish hyperparameters, which are manually set before training, from parameters, which are learned from data during training. The process involves defining a search space of possible values and considering the distribution of those values, such as uniform or log-uniform. Three essential exhaustive search techniques are manual search, grid search, and random search. While grid search is a brute-force method that tests all combinations, random search randomly tests values and can be more computationally efficient for large search spaces.