Github user yanboliang commented on the pull request:
https://github.com/apache/spark/pull/11486#issuecomment-192089154
It's a good question! It's possible that the label of input dataset is not
0 based or not continuous. So we should use ```StringIndexer``` to index label
in [0, numLabels), and after training we use ```IndexToString``` to map index
label to the original ones. We have already store the label map in the metadata
of label column.
All the models under ML package will follow this rule. For examples, if you
train ```LogisticRegression``` with the input label ```"-1, +1"``` will produce
erroneous results, you should use ```StringIndexer``` to transform labels to
```"0, 1"``` firstly.
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