[MINOR][DOCS] Fix more typos in comments/strings.

## What changes were proposed in this pull request?

This PR fixes 135 typos over 107 files:
* 121 typos in comments
* 11 typos in testcase name
* 3 typos in log messages

## How was this patch tested?

Manual.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #11689 from dongjoon-hyun/fix_more_typos.

9 files changed, 9 insertions, 9 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala b/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala
index f21b623e93..2cd94fa8f5 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala
@@ -523,7 +523,7 @@ private[ml] object FeedForwardTopology {
   /**
    * Creates a multi-layer perceptron
    * @param layerSizes sizes of layers including input and output size
-   * @param softmax wether to use SoftMax or Sigmoid function for an output layer.
+   * @param softmax whether to use SoftMax or Sigmoid function for an output layer.
    *                Softmax is default
    * @return multilayer perceptron topology
    */
diff --git a/mllib/src/main/scala/org/apache/spark/ml/attribute/attributes.scala b/mllib/src/main/scala/org/apache/spark/ml/attribute/attributes.scala
index 521d209a8f..27554acdf3 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/attribute/attributes.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/attribute/attributes.scala
@@ -481,7 +481,7 @@ object NominalAttribute extends AttributeFactory {
  * A binary attribute.
  * @param name optional name
  * @param index optional index
- * @param values optionla values. If set, its size must be 2.
+ * @param values optional values. If set, its size must be 2.
  */
 @DeveloperApi
 class BinaryAttribute private[ml] (
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala
index f8de4e2220..c8ec0c1685 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala
@@ -83,7 +83,7 @@ class RankingMetrics[T: ClassTag](predictionAndLabels: RDD[(Array[T], Array[T])]
   /**
    * Returns the mean average precision (MAP) of all the queries.
    * If a query has an empty ground truth set, the average precision will be zero and a log
-   * warining is generated.
+   * warning is generated.
    */
   lazy val meanAveragePrecision: Double = {
     predictionAndLabels.map { case (pred, lab) =>
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
index 07eb750b06..790d6b101e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
@@ -58,7 +58,7 @@ class AssociationRules private[fpm] (
   /**
    * Computes the association rules with confidence above [[minConfidence]].
    * @param freqItemsets frequent itemset model obtained from [[FPGrowth]]
-   * @return a [[Set[Rule[Item]]] containing the assocation rules.
+   * @return a [[Set[Rule[Item]]] containing the association rules.
    *
    */
   @Since("1.5.0")
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 240781bcd3..58fd010e49 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
@@ -158,7 +158,7 @@ object LinearDataGenerator {
 
   /**
    * Generate an RDD containing sample data for Linear Regression models - including Ridge, Lasso,
-   * and uregularized variants.
+   * and unregularized variants.
    *
    * @param sc SparkContext to be used for generating the RDD.
    * @param nexamples Number of examples that will be contained in the RDD.
diff --git a/mllib/src/test/scala/org/apache/spark/ml/source/libsvm/LibSVMRelationSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/source/libsvm/LibSVMRelationSuite.scala
index 71f4926290..114a238462 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/source/libsvm/LibSVMRelationSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/source/libsvm/LibSVMRelationSuite.scala
@@ -88,7 +88,7 @@ class LibSVMRelationSuite extends SparkFunSuite with MLlibTestSparkContext {
     val df = sqlContext.read.format("libsvm").load(path)
     val tempDir2 = Utils.createTempDir()
     val writepath = tempDir2.toURI.toString
-    // TODO: Remove requirement to coalesce by supporting mutiple reads.
+    // TODO: Remove requirement to coalesce by supporting multiple reads.
     df.coalesce(1).write.format("libsvm").mode(SaveMode.Overwrite).save(writepath)
 
     val df2 = sqlContext.read.format("libsvm").load(writepath)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
index cea0adc55c..28fada7053 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
@@ -496,7 +496,7 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext w
      *    features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
      *    weights = coef(glmnet(features,label, family="multinomial", alpha = 0, lambda = 0))
      *
-     * The model weights of mutinomial logstic regression in R have `K` set of linear predictors
+     * The model weights of multinomial logistic regression in R have `K` set of linear predictors
      * for `K` classes classification problem; however, only `K-1` set is required if the first
      * outcome is chosen as a "pivot", and the other `K-1` outcomes are separately regressed against
      * the pivot outcome. This can be done by subtracting the first weights from those `K-1` set
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/stat/HypothesisTestSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/stat/HypothesisTestSuite.scala
index 142b90e764..46fcebe132 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/stat/HypothesisTestSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/stat/HypothesisTestSuite.scala
@@ -144,7 +144,7 @@ class HypothesisTestSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(chi.size === numCols)
     assert(chi(1000) != null) // SPARK-3087
 
-    // Detect continous features or labels
+    // Detect continuous features or labels
     val random = new Random(11L)
     val continuousLabel =
       Seq.fill(100000)(LabeledPoint(random.nextDouble(), Vectors.dense(random.nextInt(2))))
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
index dca8ea815a..5518bdf527 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
@@ -1075,7 +1075,7 @@ object DecisionTreeSuite extends SparkFunSuite {
     assert(a.isLeaf === b.isLeaf)
     assert(a.split === b.split)
     (a.stats, b.stats) match {
-      // TODO: Check other fields besides the infomation gain.
+      // TODO: Check other fields besides the information gain.
       case (Some(aStats), Some(bStats)) => assert(aStats.gain === bStats.gain)
       case (None, None) =>
       case _ => throw new AssertionError(