Uncertainty in Predictions

Keith and Conor discuss the importance of uncertainty in predictions, contrasting kernel Ridge regression with gaussian processes. They delve into how Bayesian concepts differ from frequentist views and emphasize the value of considering the distribution of knowledge in decision-making scenarios.