Several methods exist for evaluating the calibration of regression models. Negative log likelihood (NLL) provides a standard metric for quantifying the overall quality of probabilistic models by measuring the probability of observing the data given the predicted distribution. However, NLL depends on both the predicted mean and uncertainty, and it can be useful to evaluate only the quality of the uncertainty estimates. Apart from NLL, we have also calculated the average miscalibration curve along with the miscalibration area to evaluate the predictive uncertainty of the models. The plot gives information about the model's average calibration.