Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/13176#discussion_r64299667
--- Diff: docs/ml-features.md ---
@@ -145,9 +148,11 @@ for more details on the API.
passed to other algorithms like LDA.
During the fitting process, `CountVectorizer` will select the top
`vocabSize` words ordered by
- term frequency across the corpus. An optional parameter "minDF" also
affects the fitting process
+ term frequency across the corpus. An optional parameter `minDF` also
affects the fitting process
by specifying the minimum number (or fraction if < 1.0) of documents a
term must appear in to be
- included in the vocabulary.
+ included in the vocabulary. Another optional binary toggle parameter
controls the output vector.
+ If set to true all nonzero counts are set to 1. This is especially useful
for modelling discrete
--- End diff --
After reading this again, I think you say model(ing) too many times here.
You can change ".. useful for modelling discrete .." -> ".. useful for
discrete.."
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