RVargasHdz

Hi! Yeah you could do something like this, self.GP[i] = GPR(length_scale=np.ones(self.x_dim), kernel=C(1.0, (1e-3, 1e3)) * Matern(nu=2.5), n_restarts_optimizer=self.n_rest_opt) where x_dim is the number of features and C is the constant kernel....

I had a mistake, self.GP[i] = GPR(kernel=C(1.0, (1e-3, 1e3)) * Matern(length_scale=np.ones(self.x_dim), nu=2.5), n_restarts_optimizer=self.n_rest_opt) where "length_scale=np.ones(self.x_dim)" indicates that the kernel function has independent length parameters for each individual feature.

Yeah that could work too! You will have to import all different types of kernels from the sklearn library.

Hi, I saw that you updated the coda and include the possibility to use more robust kernels, Thanks :) I should define the kernel function using the standard notation from...