CODEWITHSUNDEEP
matlabmachine-learningregressiondata-mininggaussian
Arvind Kumar
Arvind Kumar

Reputation: 66

initial seed for sparse GP regression

I use the sparse Gaussian process for regression from Rasmussen. [http://www.tsc.uc3m.es/~miguel/downloads.php][1]

The syntax for predicting the mean is:

[~, mu_1, ~, ~, loghyper] = ssgpr_ui(Xtrain, Ytrain, Xtest, Ytest, m);

My question is, the author states that the initial hyper parameter search condition is different for different iterations, hence the results of the model is different from every iteration. Is there any way to ensure that the best initialization or seed condition is set to have good quality hyper parameters for best predictions and reproducible results?

Upvotes: 0

Views: 50

Answers (1)

Arvind Kumar
Arvind Kumar

Reputation: 66

In order to obtain the same predictions every time, it is possible to set the seed by stream = RandStream('mt19937ar','Seed',123456); RandStream.setGlobalStream(stream);

However, there is no standard procedure to set the best seed. Doing so will lead to over fitting of the model as we are giving too much of ideal conditions to fit the training data as quoted by @mikkola

Upvotes: 1

Related Questions