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It starts to predict the same value as I increase sample number to predict #35
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It's really tough to say based on this information. Can you share the data? |
Thank you very much for your answer. Yes, I can share, of course. samplesize=1000 I train on X (1000 input combinations) and three different model outputs OB[:,s], for s=[0,1,2], and I test on Xt with the three model outputs OBt[:,s] Up to X=X[:400] OB=OB[:400] any values predicted is different one another and it seems to work quite well; from X=X[:500] OB=OB[:500] on it predicts increasingly more identical values at the bottom of the prediction lists of 100 test samples. thank you. |
Hi Matteo, Capaulson I am trying to use a dataset with ~7000 samples having 8 Xs and 1 Y. I can train the dataset but I am not sure how to save the model. I went through the scripts but don't see any option to save the model. If I use regression kriging, I don't the plot function or save figure function. So, how do I actually plot the actual vs predicted values of Y or even the error in them? I think I will need to do the plotting outside the standard PyKriging library by extracting the predicted Y if using regression kriging. What I am essentially trying to understand is:
Appreciate your insights. Best Regards, |
Hi, sorry for bothering you.
I have been dealing with the following problem for 3 months, hence I decided to try to share it.
I am training kriging and xgboost on sets of increasing sample number ( say from 100 samples to 1000 samples) of 15 inputs and 1 output each sample and I use the trained functions to predict a test set (that is always the same).
Whilst I haven't ever had any problem with xgboost prediction, when I predict the test set with kriging, I have no problems up to 400ish samples training; the more I increase from say around 400 samples to train, the more the last values of the test set which I predict equal the same value.
Have you got any idea about that?
Thanks!
Matteo
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