The document explores the process of hyperparameter optimization in machine learning, drawing an analogy to selecting jeans that meet specific criteria. It emphasizes the importance of optimizing learning algorithms by minimizing error and maximizing performance through hyperparameter tuning, which involves setting values that control the learning process and are not learned from training data. The Comet machine learning platform is introduced as a tool for tracking, monitoring, and optimizing experiments, with a focus on its Optimizer class, which dynamically identifies optimal hyperparameter values. Three popular optimization algorithms—Bayesian optimization, grid search, and random search—are discussed, each with its specifications and use cases. An end-to-end example using a Random Forest classifier and Bayesian optimization illustrates the application of these concepts, demonstrating how Comet's platform can enhance experiment reproducibility and model performance evaluation through detailed logging and analysis tools.