Zero predicted variance while prediction differs from ground truth

[The-Alley]

Hi,

First I wanted to apologize since the reason I am asking this question might be that I do not understand kriging properly.

I have been using Kriging, KPLS, and KPLSK for multi-output regression. I have noticed that as the number of training samples increases (more than 400 in my case), the predicted variances become zero while the predictions do not completely match the ground truth. I know that the variances for training points are zero but the points I am trying to predict are not in the training dataset. Even when the prediction error is quite large, the predicted variance is still zero. I would appreciate it if someone could help me understand the reason for that. Thanks in advance.

[relf]

Hi. First what is the dimension of your training samples?
Your surrogates may suffer from overfitting as you experience degradation with number of samples.

If you have numerous samples, you may want to consider them as being noisy or even consider using a sparse GP surrogate to avoid fitting exactly the training data.