Github user feynmanliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/8304#discussion_r37439565
--- Diff: docs/ml-guide.md ---
@@ -645,6 +645,13 @@ An important task in ML is *model selection*, or using
data to find the best mod
Currently, `spark.ml` supports model selection using the
[`CrossValidator`](api/scala/index.html#org.apache.spark.ml.tuning.CrossValidator)
class, which takes an `Estimator`, a set of `ParamMap`s, and an
[`Evaluator`](api/scala/index.html#org.apache.spark.ml.Evaluator).
`CrossValidator` begins by splitting the dataset into a set of *folds*
which are used as separate training and test datasets; e.g., with `$k=3$`
folds, `CrossValidator` will generate 3 (training, test) dataset pairs, each of
which uses 2/3 of the data for training and 1/3 for testing.
`CrossValidator` iterates through the set of `ParamMap`s. For each
`ParamMap`, it trains the given `Estimator` and evaluates it using the given
`Evaluator`.
+
+The `Evaluator` can be a
[`RegressionEvaluator`](api/scala/index.html#org.apache.spark.ml.RegressionEvaluator)
+for regression problems, a
[`BinaryClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.BinaryClassificationEvaluator)
+for binary data or a
[`MultiClassClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.MultiClassClassificationEvaluator)
--- End diff --
nit: Oxford comma ("...binary data, or a...")
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