title: Alternative solution similar to Keras kfold for multi input multi output
body: <p>I have a flow past airfoil problem involving 3 inputs (AOA,x,y) and 3 outputs (u,v,p). It's working well when I use train_test_split.</p>
  
  <p>Then, I wanted to try using Keras kFold to validate my overall prediction accuracy:</p>
  
  <pre><code>...
  input_var = np.vstack((AOA, x, y)).T
  output_var = np.vstack((u,v,p)).T
  
  # define 10-fold cross validation test harness
  kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed_value)
  cvscores = []
  for train, test in kfold.split(input_var, output_var):
  
      # create model
  ...
  </code></pre>
  
  <p>However I got the error:</p>
  
  <pre><code>ValueError: Supported target types are: ('binary', 'multiclass'). Got 'continuous-multioutput' instead.
  </code></pre>
  
  <p>So is there some error? Or multi input output is not supported at all.</p>
  
  <p>I am thinking of an alternative solution of looping manually and using train_test_split with a different random state each time, so as to select a different test data every time. Then computer the overall mean MSE.</p>
  
  <p>Is this logical? Will I achieve the same effect as kFold?</p>
  
  <p>Thanks</p>