title: Upweight a Category
body: <p>I have built a TensorFlow model that uses a <code>DNNClassifier</code> to classify input into two categories.</p>
  
  <p>My problem is that Outcome 1 occurs upwards of 90-95% of the time. Therefore, TensorFlow is giving me the same probabilities for all of my predictions.</p>
  
  <p>I am trying to predict the other outcome (e.g. having a false positive for Outcome 2 is preferable to missing a possible occurrence of Outcome 2). I know that in machine learning in general, in this case it would be worthwhile to try to upweight Outcome 2.</p>
  
  <p>However, I don't know how to do this in TensorFlow. The <a href="https://www.tensorflow.org/api_docs/python/tf/estimator/DNNClassifier#config" rel="nofollow noreferrer">documentation</a> alludes to it being possible, but I can't find any examples of what it would actually look like. Has anyone has successfully done this, or does anyone know where I could find some example code or a thorough explanation (I'm using Python)?</p>
  
  <p>Note: I have seen exposed weights being manipulated when someone is using the more fundamental parts of TensorFlow and not an estimator. For maintenance reasons, I need to do this using an estimator.</p>