update doc

mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala

@@ -52,10 +52,12 @@ private[ml] trait CrossValidatorParams extends Params {
   def getEstimatorParamMaps: Array[ParamMap] = $(estimatorParamMaps)
 
   /**
-   * param for the evaluator for selection
+   * param for the evaluator used to select hyper-parameters that maximize the cross-validated
+   * metric
    * @group param
    */
-  val evaluator: Param[Evaluator] = new Param(this, "evaluator", "evaluator for selection")
+  val evaluator: Param[Evaluator] = new Param(this, "evaluator",
+    "evaluator used to select hyper-parameters that maximize the cross-validated metric")
 
   /** @group getParam */
   def getEvaluator: Evaluator = $(evaluator)

python/pyspark/ml/pipeline.py

@@ -73,7 +73,7 @@ def transform(self, dataset, params={}):
 @inherit_doc
 class Model(Transformer):
     """
-    Abstract class for models that fitted by estimators.
+    Abstract class for models that are fitted by estimators.
     """
 
     __metaclass__ = ABCMeta
@@ -163,7 +163,7 @@ def fit(self, dataset, params={}):
 
 
 @inherit_doc
-class PipelineModel(Transformer):
+class PipelineModel(Model):
     """
     Represents a compiled pipeline with transformers and fitted models.
     """

python/pyspark/ml/tuning.py

@@ -116,7 +116,9 @@ class CrossValidator(Estimator):
     estimatorParamMaps = Param(Params._dummy(), "estimatorParamMaps", "estimator param maps")
 
     # a placeholder to make it appear in the generated doc
-    evaluator = Param(Params._dummy(), "evaluator", "evaluator for selection")
+    evaluator = Param(
+        Params._dummy(), "evaluator",
+        "evaluator used to select hyper-parameters that maximize the cross-validated metric")
 
     # a placeholder to make it appear in the generated doc
     numFolds = Param(Params._dummy(), "numFolds", "number of folds for cross validation")
@@ -131,8 +133,11 @@ def __init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, numF
         self.estimator = Param(self, "estimator", "estimator to be cross-validated")
         #: param for estimator param maps
         self.estimatorParamMaps = Param(self, "estimatorParamMaps", "estimator param maps")
-        #: param for evaluator for selection
-        self.evaluator = Param(self, "evaluator", "evaluator for selection")
+        #: param for the evaluator used to select hyper-parameters that
+        #: maximize the cross-validated metric
+        self.evaluator = Param(
+            self, "evaluator",
+            "evaluator used to select hyper-parameters that maximize the cross-validated metric")
         #: param for number of folds for cross validation
         self.numFolds = Param(self, "numFolds", "number of folds for cross validation")
         self._setDefault(numFolds=3)
@@ -228,7 +233,7 @@ def fit(self, dataset, params={}):
 
 class CrossValidatorModel(Model):
     """
-    Model from k-fold corss validation.
+    Model from k-fold cross validation.
     """
 
     def __init__(self, bestModel):