I am reading about the Google Prediction API and can't figure out a part of the docs.
From the use cases I am stuck a bit on this part:
Each line can only have one label assigned, but you can apply multiple labels to one example by repeating an example and applying different labels to each one. For example: "excited", "OMG! Just had a fabulous day!" "annoying", "OMG! Just had a fabulous day!" If you send a tweet to this model, you might get a classification something like this: "excited":0.6, "annoying":0.2.
Why would it put "excited":0.6, "annoying":0.2 while there are no more features on excited. Why is excited prefered?