Gaussian Process Regression

Gaussian Process Regression (GPR) is a flexible statistical method used to predict outcomes based on known data. It treats the unknown function you want to understand as a smooth, probabilistic curve, where predictions come with a measure of confidence. Instead of assuming a fixed form, GPR considers all possible curves that fit the data, weighted by how well they match. This approach allows it to deliver both predictions and uncertainty estimates, making it useful for modeling complex relationships where data may be noisy or incomplete.