# Sample size needed for Gaussian process classification

I recently read the paper by Loeppky et al. on Choosing the sample size of a computer experiment: a practical guide and was curious to know if there were rules of thumb about the sample size considerations for Gaussian process (GP) classification? The paper suggests a value of $n=10\times d$ where $d$ is the dimension of the input space, however, this result is for GP regression and not the GP classification case. I imagine classification probably requires more points since it seems to be a harder problem but am not sure if anything has been published on the topic. If it matters, in my application the dimension of the input space is NEVER larger than 1.

• I'm not sure this is really knowable (but I'm not voting to close). If the signal is strong and the model correctly specified, the problem is much easier (can get away with a smaller sample size) than a problem with many irrelevant features and weak signal.
– Sycorax
Apr 6, 2016 at 18:59
• @C11H17N2O2SNa I am just asking for a rule of thumb not a hard number on the sample size. Apr 6, 2016 at 19:09
• GPs will give you the uncertainty in your predictions (provided a good prior and kernel selection). That gives you a tool to estimate how many samples you need until you get to the uncertainty level you want. Not aware of a rule of thumb.
– Bar
Oct 13, 2017 at 10:38