address comment

Author: huaxingao

mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala

@@ -71,12 +71,12 @@ class VectorAssembler @Since("1.4.0") (@Since("1.4.0") override val uid: String)
    */
   @Since("2.4.0")
   override val handleInvalid: Param[String] = new Param[String](this, "handleInvalid",
-    """Param for how to handle invalid data (NULL values). Options are 'skip' (filter out rows with
-      |invalid data), 'error' (throw an error), or 'keep' (return relevant number of NaN in the
-      |output). Column lengths are taken from the size of ML Attribute Group, which can be set using
-      |`VectorSizeHint` in a pipeline before `VectorAssembler`. Column lengths can also be inferred
-      |from first rows of the data since it is safe to do so but only in case of 'error' or 'skip'.
-      |""".stripMargin.replaceAll("\n", " "),
+    """Param for how to handle invalid data (NULL and NaN values). Options are 'skip' (filter out
+      |rows with invalid data), 'error' (throw an error), or 'keep' (return relevant number of NaN
+      |in the output). Column lengths are taken from the size of ML Attribute Group, which can be
+      |set using `VectorSizeHint` in a pipeline before `VectorAssembler`. Column lengths can also
+      |be inferred from first rows of the data since it is safe to do so but only in case of 'error'
+      |or 'skip'.""".stripMargin.replaceAll("\n", " "),
     ParamValidators.inArray(VectorAssembler.supportedHandleInvalids))
 
   setDefault(handleInvalid, VectorAssembler.ERROR_INVALID)

python/pyspark/ml/feature.py

@@ -2745,13 +2745,13 @@ class VectorAssembler(JavaTransformer, HasInputCols, HasOutputCol, HasHandleInva
     """
 
     handleInvalid = Param(Params._dummy(), "handleInvalid", "How to handle invalid data (NULL " +
-                          "values). Options are 'skip' (filter out rows with invalid data), " +
-                          "'error' (throw an error), or 'keep' (return relevant number of NaN in " +
-                          "the output). Column lengths are taken from the size of ML Attribute " +
-                          "Group, which can be set using `VectorSizeHint` in a pipeline before " +
-                          "`VectorAssembler`. Column lengths can also be inferred from first " +
-                          "rows of the data since it is safe to do so but only in case of " +
-                          "'error' or 'skip').",
+                          "and NaN values). Options are 'skip' (filter out rows with invalid " +
+                          "data), 'error' (throw an error), or 'keep' (return relevant number " +
+                          "of NaN in the output). Column lengths are taken from the size of ML " +
+                          "Attribute Group, which can be set using `VectorSizeHint` in a " +
+                          "pipeline before `VectorAssembler`. Column lengths can also be " +
+                          "inferred from first rows of the data since it is safe to do so but " +
+                          "only in case of 'error' or 'skip').",
                           typeConverter=TypeConverters.toString)
 
     @keyword_only