[SPARK-15997][DOC][ML] Update user guide for HashingTF, QuantileVectorizer and CountVectorizer

## What changes were proposed in this pull request?

Made changes to HashingTF,QuantileVectorizer and CountVectorizer

Author: GayathriMurali <gayathri.m@intel.com>

Closes #13745 from GayathriMurali/SPARK-15997.

1 files changed, 7 insertions, 2 deletions

diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/QuantileDiscretizerExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/QuantileDiscretizerExample.scala
index 1a16515594..2f7e217b8f 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/QuantileDiscretizerExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/QuantileDiscretizerExample.scala
@@ -32,8 +32,13 @@ object QuantileDiscretizerExample {
 
     // $example on$
     val data = Array((0, 18.0), (1, 19.0), (2, 8.0), (3, 5.0), (4, 2.2))
-    val df = spark.createDataFrame(data).toDF("id", "hour")
-
+    var df = spark.createDataFrame(data).toDF("id", "hour")
+    // $example off$
+    // Output of QuantileDiscretizer for such small datasets can depend on the number of
+    // partitions. Here we force a single partition to ensure consistent results.
+    // Note this is not necessary for normal use cases
+        .repartition(1)
+    // $example on$
     val discretizer = new QuantileDiscretizer()
       .setInputCol("hour")
       .setOutputCol("result")