A probabilistic regression model predicts both an estimated value and the uncertainty associated with that prediction. Rather than returning only a point estimate, it represents uncertainty in parameters or outputs, such as a predicted value with a standard deviation.
A regression model that uses not only the weights for each feature, but also the uncertainty of those weights. A probabilistic regression model generates a prediction and the uncertainty of that prediction. For example, a probabilistic regression model might yield a prediction of 325 with a standard deviation of 12. For more information about probabilistic regression models, see this Colab on tensorflow.org.