Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/13745#discussion_r68183527
--- Diff: examples/src/main/python/ml/quantile_discretizer_example.py ---
@@ -29,11 +29,12 @@
# $example on$
data = [(0, 18.0,), (1, 19.0,), (2, 8.0,), (3, 5.0,), (4, 2.2,)]
dataFrame = spark.createDataFrame(data, ["id", "hour"])
-
- # Note that we compute exact quantiles here by setting `relativeError`
to 0 for
- # illustrative purposes, however in most cases the default parameter
value should suffice
- discretizer = QuantileDiscretizer(numBuckets=3, inputCol="hour",
outputCol="result",
- relativeError=0)
+ # $example off$
+ # Output of QuantileDiscretizer for such small datasets differ wrt
underlying cores.
+ # Allocating single partition for the dataframe helps with consistent
results.
+ .repartition(1)
--- End diff --
Also can we just call it `df` to match the other examples (i just noticed
that)
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