GP Regression Model Predicting very similar numbers for all test inputs

Question

I'm using doc2vec embeddings of amino acid sequences to try and predict kinetic rate.

I've tried both standardising and not standardising my input vectors (X) but unless I standardise my output variable (kinetic rates), my GP model predicts very similar numbers for all the test inputs (between 4.87 and 4.9)?

Are you supposed to standardise your output values or is there something wrong with my model?

I'm using the GPy package in Python.

This is my code:

#GP Regression for word vectors
def Gp_regression(Xtrain, Ytrain, Xtest, Ytest):

    kernel = GPy.kern.RBF(input_dim = 64, variance = 1, lengthscale =    1)
    m = GPy.models.GPRegression(Xtrain, Ytrain, kernel=kernel,    noise_var=1e-10)
    m.optimize_restarts(num_restarts = 10)

    Xtest = np.ndarray(shape=(1,64))
    mean = m.predict(Xtest)

return mean