SPARK-3359 [CORE] [DOCS] `sbt/sbt unidoc` doesn't work with Java 8

These are more `javadoc` 8-related changes I spotted while investigating. These should be helpful in any event, but this does not nearly resolve SPARK-3359, which may never be feasible while using `unidoc` and `javadoc` 8.

Author: Sean Owen <sowen@cloudera.com>

Closes #4193 from srowen/SPARK-3359 and squashes the following commits:

5b33f66 [Sean Owen] Additional scaladoc fixes for javadoc 8; still not going to be javadoc 8 compatible

5 files changed, 12 insertions, 12 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala
index fe39cd1bc0..bb291e6e1f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala
@@ -58,11 +58,11 @@ abstract class PipelineStage extends Serializable with Logging {
 /**
  * :: AlphaComponent ::
  * A simple pipeline, which acts as an estimator. A Pipeline consists of a sequence of stages, each
- * of which is either an [[Estimator]] or a [[Transformer]]. When [[Pipeline.fit]] is called, the
- * stages are executed in order. If a stage is an [[Estimator]], its [[Estimator.fit]] method will
+ * of which is either an [[Estimator]] or a [[Transformer]]. When [[Pipeline#fit]] is called, the
+ * stages are executed in order. If a stage is an [[Estimator]], its [[Estimator#fit]] method will
  * be called on the input dataset to fit a model. Then the model, which is a transformer, will be
  * used to transform the dataset as the input to the next stage. If a stage is a [[Transformer]],
- * its [[Transformer.transform]] method will be called to produce the dataset for the next stage.
+ * its [[Transformer#transform]] method will be called to produce the dataset for the next stage.
  * The fitted model from a [[Pipeline]] is an [[PipelineModel]], which consists of fitted models and
  * transformers, corresponding to the pipeline stages. If there are no stages, the pipeline acts as
  * an identity transformer.
@@ -77,9 +77,9 @@ class Pipeline extends Estimator[PipelineModel] {
 
   /**
    * Fits the pipeline to the input dataset with additional parameters. If a stage is an
-   * [[Estimator]], its [[Estimator.fit]] method will be called on the input dataset to fit a model.
+   * [[Estimator]], its [[Estimator#fit]] method will be called on the input dataset to fit a model.
    * Then the model, which is a transformer, will be used to transform the dataset as the input to
-   * the next stage. If a stage is a [[Transformer]], its [[Transformer.transform]] method will be
+   * the next stage. If a stage is a [[Transformer]], its [[Transformer#transform]] method will be
    * called to produce the dataset for the next stage. The fitted model from a [[Pipeline]] is an
    * [[PipelineModel]], which consists of fitted models and transformers, corresponding to the
    * pipeline stages. If there are no stages, the output model acts as an identity transformer.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index ddca30c3c0..53b7970470 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -151,10 +151,10 @@ class RowMatrix(
    * storing the right singular vectors, is computed via matrix multiplication as
    * U = A * (V * S^-1^), if requested by user. The actual method to use is determined
    * automatically based on the cost:
-   *  - If n is small (n < 100) or k is large compared with n (k > n / 2), we compute the Gramian
-   *    matrix first and then compute its top eigenvalues and eigenvectors locally on the driver.
-   *    This requires a single pass with O(n^2^) storage on each executor and on the driver, and
-   *    O(n^2^ k) time on the driver.
+   *  - If n is small (n < 100) or k is large compared with n (k > n / 2), we compute
+   *    the Gramian matrix first and then compute its top eigenvalues and eigenvectors locally
+   *    on the driver. This requires a single pass with O(n^2^) storage on each executor and
+   *    on the driver, and O(n^2^ k) time on the driver.
    *  - Otherwise, we compute (A' * A) * v in a distributive way and send it to ARPACK's DSAUPD to
    *    compute (A' * A)'s top eigenvalues and eigenvectors on the driver node. This requires O(k)
    *    passes, O(n) storage on each executor, and O(n k) storage on the driver.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
index 951733fada..f1a6ed2301 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
@@ -183,7 +183,7 @@ private[tree] object DecisionTreeMetadata extends Logging {
   }
 
   /**
-   * Version of [[buildMetadata()]] for DecisionTree.
+   * Version of [[DecisionTreeMetadata#buildMetadata]] for DecisionTree.
    */
   def buildMetadata(
       input: RDD[LabeledPoint],
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala
index 4bca9039eb..e1169d9f66 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala
@@ -45,7 +45,7 @@ trait Loss extends Serializable {
    * purposes.
    * @param model Model of the weak learner.
    * @param data Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
-   * @return
+   * @return Measure of model error on data
    */
   def computeError(model: TreeEnsembleModel, data: RDD[LabeledPoint]): Double
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
index 69299c2198..97f54aa62d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
@@ -62,7 +62,7 @@ object LinearDataGenerator {
    * @param nPoints Number of points in sample.
    * @param seed Random seed
    * @param eps Epsilon scaling factor.
-   * @return
+   * @return Seq of input.
    */
   def generateLinearInput(
       intercept: Double,