title: How to generate Scikit-Learn Gaussian Process regression with 2D input, 1D output
body: <p>I have been looking for the answer to my question for quite a while. No Luck so far :(. I will my question as simple as possible. For the simplicity I only have a 2D input(it will eventually grow). Lets say I am using two variables (feature : Vehicle Odometer measurement, New Car Price) to predict a value of the car (target : Old car price) How can I train sklearn.gaussian_process.GaussianProcessRegressor to predict what I am looking for.</p>
  
  <pre><code>from sklearn import gaussian_process
  
  
  X_train = np.array(X).reshape((-1, 2)).astype(int)
  
  y_train = np.array(y).reshape(-1,1).astype(int)
  
  GPR = gaussian_process.GaussianProcessRegressor(normalize_y = False,n_restarts_optimizer = 3)
  
  GPR.fit(X_train,y_train)
  #creating random points for testing the data
  X_test_Odometer = np.linspace(0, 268000, 1000)[:, None]
  X_test_Price = random.sample(range(5000, 13000), 1000)
  X_test = np.column_stack((X_test_Odometer,X_test_Price)).astype(int)
  
  GPR.predict(X_test)
  </code></pre>
  
  <p>This prediction doesnot work at all. I do not know whether I need to customize a kernel. If yes, I do not know how to. I am new to scikit and any help would be appreciated :)</p>