Building Confidence Into Your Model: Gaussian Processes for Intelligent Hyperparameter Search

Gaussian Processes solve a critical pain point in machine learning: knowing not just what to predict, but how confident you should be. By quantifying uncertainty, GPs enable Bayesian optimization to intelligently guide your search through hyperparameter space, turning expensive trial-and-error into a principled strategy that learns where to look next.