What is the purpose of hyperparameter tuning in machine learning?

Hyperparameter tuning is a critical aspect of machine learning that aims to optimize the performance of a model by determining the most effective settings for its hyperparameters. Hyperparameters are extra parameters that are set before the learning process begins and significantly influence how a model learns from the data. Examples include learning rates, number of trees in a random forest, or the number of layers in a neural network.

The tuning process involves systematically trying different combinations of hyperparameters and evaluating how well the model performs with each configuration, typically through techniques such as grid search, random search, or more advanced methods like Bayesian optimization. The ultimate goal is to improve the model's accuracy, reduce overfitting, or enhance generalization—thereby ensuring the model performs well on unseen data.

In contrast, finalizing preprocessing steps, creating visualizations, or reducing dataset sizes serve different purposes in the workflow of machine learning but do not directly contribute to adjusting the model's hyperparameters for performance enhancement, which is central to hyperparameter tuning.