fitgpr gaussian regression parameters
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Sara Hamdan
2020 年 4 月 5 日
コメント済み: israt fatema
2022 年 4 月 28 日
Hello guys,
I am using Gaussian Process Regression 'fitgpr' to fit a model to my data (~ 17,500 x 3 input, 17,500 x1 output), it works perfect (very low loss value).
I want to compare with the polynomial fitting, I have the number of coefficients in polynomials but how to get them in fitgpr?
I believe the 'coefficients' in Gaussian are the mean & variance (right?) how to get them?
(Any help is appreciated, I am a bit confused, thank you)
Regards,
Sara
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Thiago Henrique Gomes Lobato
2020 年 4 月 5 日
A Gaussian Process Regression is a non-parametric model, which means it heavily depends of the training data you use. This means that, in this case, every training point will have a coefficient of it's own for the covariance/kernel function, which makes a comparison between Gaussian Process coefficients and Polynomial Coefficients impractical. For a better understanding of the method I would strongely suggest the following online book http://www.gaussianprocess.org/gpml/, specially the chapter 2 which focus on regression.
If you want to compare both methods my main recomendation is to used cross-validation. The simpliest way to do it is to divide your training data in two sets, a training and a validation one. You can then train both models in the training set and evaluate the loss in the test set. A Gaussian process is able to represent any dataset with 0 error depeding of your parameters, thus a cross validation is needed to really be able know if the model is good enough to be generalized.
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israt fatema
2022 年 4 月 28 日
How can i use other evaluation metrics (https://au.mathworks.com/help/stats/gaussian-process-regressionmodels.html?searchHighlight=gaussian%20process%20regression%20model&s_tid=srchtitle_gaussian%2520process%2520regression%2520model_1)
for Gaussian process regression model, i.e CRPS or prediction interval coverage probability (PICP)?
その他の回答 (1 件)
hichem tahraoui
2020 年 7 月 25 日
Hello
if i understood, you want to know the amount of parameters that was used in SVM, you can use the code from matlab `` Quantity_of_support_vectors = size (model.SupportVectors, 1) ''
on the other hand in the Gaussian process, I did not find how to obtain the number of parameters. it's always the same problem
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