title: The data set being scored must contain all features used during training, missing feature(s)
body: <p>I built a model in Azure ML, then i published this model as a web service. I would like to customize the input fields of the web service by updating the needed inputs for the model prediction WS.</p>
  
  <p>The model has been trained on set of features to predict a price value on a given date. I want the customer to provide a date to predict the price without the need to enter features values that i supplied when i trained the model.</p>
  
  <p>The error message when i customize the web service inputs by removing the unneeded columns in the predictive experiment (by adding select column module before the score module):</p>
  
  <blockquote>
    <p>Error 1000: <br/> AFx Library library exception: table: The data set
    being scored must contain all features used during training, missing
    feature(s).</p>
  </blockquote>
  
  <p>How would i fix this issue?</p>