Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/9843#discussion_r46010330
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala ---
@@ -218,7 +218,7 @@ private object IDFModel {
newValues(k) = values(k) * idf(indices(k))
k += 1
}
- Vectors.sparse(n, indices, newValues)
+ Vectors.sparse(n, indices, newValues).toSparse
--- End diff --
Most of mllib codebase is using
```scala
def sparse(size: Int, indices: Array[Int], values: Array[Double]): Vector =
```
which will take the raw arrays and construct the sparse vector. The `Seq`
version is rarely used.
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