What is the purpose of Hyperparameter Tuning in machine learning?

Hyperparameter tuning is a critical technique in machine learning that focuses on optimizing the performance of models by adjusting parameters that govern the training process. These hyperparameters are not learned from the data during training; instead, they are set prior to the training phase and can significantly influence the model's ability to generalize well to unseen data.

By systematically exploring different combinations of hyperparameters—such as learning rate, batch size, and regularization strength—practitioners can find the optimal settings that lead to better accuracy, reduced overfitting, and improved overall performance of the model. This process can involve methods like grid search or random search, and may also incorporate more advanced optimization techniques like Bayesian optimization.

In contrast, the other options focus on aspects unrelated to hyperparameter tuning. Improving data visualization techniques pertains to how data insights are presented rather than the model training process. Converting unstructured data into structured formats relates to data preprocessing, which occurs before model training and isn't tied to tuning model parameters. Creating chatbots based on user intents is more aligned with natural language processing and application development rather than the optimization of machine learning models.