title: If I run a prediction against a model with multiple categories, are the scores split amongst all categories?
body: <p>Let's say I have created a model with ~30 items for each of 10 categories. I've taken all of the defaults that were provided to me.</p>
  
  <p>The Average F1 Score for the model is 0.875 (I have 2 categories that are very closely related, so that's hurting accuracy a bit).</p>
  
  <p>If I do a real-time prediction for a piece of text that should match positively for category 3 and 8, I get this result:</p>
  
  <pre><code>{
      "Prediction": {
          "details": {
              "Algorithm": "SGD",
              "PredictiveModelType": "MULTICLASS"
          },
          "predictedLabel": "8",
          "predictedScores": {
              "1": 0.002642059000208974,
              "2": 0.010648942552506924,
              "3": 0.41401588916778564,
              "4": 0.02918998710811138,
              "5": 0.008376320824027061,
              "6": 0.009010250680148602,
              "7": 0.006029266398400068,
              "8": 0.4628857374191284,
              "9": 0.04102163389325142,
              "10": 0.01617990992963314
          }
      }
  }
  </code></pre>
  
  <p>What I'm wondering is whether 3 &amp; 8 both had effectively an ~80% certainty, but because they both matched the certainty was split between the two. If you sum all the <code>predictedScores</code>, you get .999999997, which has me questioning whether there's a total 1.0 score that gets split amongst each of the available categories...</p>
  
  <p>If I instead set up 10 different models, and did binary matches against each of them independently, would I see that 3 &amp; 8 would score higher (e.g. something closer to 0.8)?</p>
  
  <p>I guess a related question, that I don't really need answered but might help clarify the overall question, is ... If I had a theoretical piece of text that definitely fit all 10 categories, could Amazon Machine Learning respond with a <code>predictedScore</code> value of 1.0 for each category? Or, because the maximum <code>predictedScore</code> is 1.0, would it return 0.1 for each category?</p>