[SPARK-16403][EXAMPLES] Cleanup to remove unused imports, consistent style, minor fixes

## What changes were proposed in this pull request?

Cleanup of examples, mostly from PySpark-ML to fix minor issues:  unused imports, style consistency, pipeline_example is a duplicate, use future print funciton, and a spelling error.

* The "Pipeline Example" is duplicated by "Simple Text Classification Pipeline" in Scala, Python, and Java.

* "Estimator Transformer Param Example" is duplicated by "Simple Params Example" in Scala, Python and Java

* Synced random_forest_classifier_example.py with Scala by adding IndexToString label converted

* Synced train_validation_split.py (in Scala ModelSelectionViaTrainValidationExample) by adjusting data split, adding grid for intercept.

* RegexTokenizer was doing nothing in tokenizer_example.py and JavaTokenizerExample.java, synced with Scala version

## How was this patch tested?
local tests and run modified examples

Author: Bryan Cutler <cutlerb@gmail.com>

Closes #14081 from BryanCutler/examples-cleanup-SPARK-16403.

56 files changed, 142 insertions, 646 deletions

diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaPipelineExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaPipelineExample.java
index 9a43189c91..4ccd8f6ce2 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaPipelineExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaPipelineExample.java
@@ -60,7 +60,7 @@ public class JavaPipelineExample {
       .setOutputCol("features");
     LogisticRegression lr = new LogisticRegression()
       .setMaxIter(10)
-      .setRegParam(0.01);
+      .setRegParam(0.001);
     Pipeline pipeline = new Pipeline()
       .setStages(new PipelineStage[] {tokenizer, hashingTF, lr});
 
@@ -71,7 +71,7 @@ public class JavaPipelineExample {
     Dataset<Row> test = spark.createDataFrame(Arrays.asList(
       new JavaDocument(4L, "spark i j k"),
       new JavaDocument(5L, "l m n"),
-      new JavaDocument(6L, "mapreduce spark"),
+      new JavaDocument(6L, "spark hadoop spark"),
       new JavaDocument(7L, "apache hadoop")
     ), JavaDocument.class);
 
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java
deleted file mode 100644
index ca80d0d8bb..0000000000
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java
+++ /dev/null
@@ -1,113 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.examples.ml;
-
-import java.util.List;
-
-import com.google.common.collect.Lists;
-
-import org.apache.spark.ml.classification.LogisticRegressionModel;
-import org.apache.spark.ml.param.ParamMap;
-import org.apache.spark.ml.classification.LogisticRegression;
-import org.apache.spark.mllib.linalg.Vectors;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.sql.Dataset;
-import org.apache.spark.sql.Row;
-import org.apache.spark.sql.SparkSession;
-
-/**
- * A simple example demonstrating ways to specify parameters for Estimators and Transformers.
- * Run with
- * {{{
- * bin/run-example ml.JavaSimpleParamsExample
- * }}}
- */
-public class JavaSimpleParamsExample {
-
-  public static void main(String[] args) {
-    SparkSession spark = SparkSession
-      .builder()
-      .appName("JavaSimpleParamsExample")
-      .getOrCreate();
-
-    // Prepare training data.
-    // We use LabeledPoint, which is a JavaBean. Spark SQL can convert RDDs of JavaBeans
-    // into DataFrames, where it uses the bean metadata to infer the schema.
-    List<LabeledPoint> localTraining = Lists.newArrayList(
-      new LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)),
-      new LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)),
-      new LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)),
-      new LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5)));
-    Dataset<Row> training =
-      spark.createDataFrame(localTraining, LabeledPoint.class);
-
-    // Create a LogisticRegression instance. This instance is an Estimator.
-    LogisticRegression lr = new LogisticRegression();
-    // Print out the parameters, documentation, and any default values.
-    System.out.println("LogisticRegression parameters:\n" + lr.explainParams() + "\n");
-
-    // We may set parameters using setter methods.
-    lr.setMaxIter(10)
-      .setRegParam(0.01);
-
-    // Learn a LogisticRegression model. This uses the parameters stored in lr.
-    LogisticRegressionModel model1 = lr.fit(training);
-    // Since model1 is a Model (i.e., a Transformer produced by an Estimator),
-    // we can view the parameters it used during fit().
-    // This prints the parameter (name: value) pairs, where names are unique IDs for this
-    // LogisticRegression instance.
-    System.out.println("Model 1 was fit using parameters: " + model1.parent().extractParamMap());
-
-    // We may alternatively specify parameters using a ParamMap.
-    ParamMap paramMap = new ParamMap();
-    paramMap.put(lr.maxIter().w(20)); // Specify 1 Param.
-    paramMap.put(lr.maxIter(), 30); // This overwrites the original maxIter.
-    double[] thresholds = {0.5, 0.5};
-    paramMap.put(lr.regParam().w(0.1), lr.thresholds().w(thresholds)); // Specify multiple Params.
-
-    // One can also combine ParamMaps.
-    ParamMap paramMap2 = new ParamMap();
-    paramMap2.put(lr.probabilityCol().w("myProbability")); // Change output column name.
-    ParamMap paramMapCombined = paramMap.$plus$plus(paramMap2);
-
-    // Now learn a new model using the paramMapCombined parameters.
-    // paramMapCombined overrides all parameters set earlier via lr.set* methods.
-    LogisticRegressionModel model2 = lr.fit(training, paramMapCombined);
-    System.out.println("Model 2 was fit using parameters: " + model2.parent().extractParamMap());
-
-    // Prepare test documents.
-    List<LabeledPoint> localTest = Lists.newArrayList(
-        new LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
-        new LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)),
-        new LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5)));
-    Dataset<Row> test = spark.createDataFrame(localTest, LabeledPoint.class);
-
-    // Make predictions on test documents using the Transformer.transform() method.
-    // LogisticRegressionModel.transform will only use the 'features' column.
-    // Note that model2.transform() outputs a 'myProbability' column instead of the usual
-    // 'probability' column since we renamed the lr.probabilityCol parameter previously.
-    Dataset<Row> results = model2.transform(test);
-    Dataset<Row> rows = results.select("features", "label", "myProbability", "prediction");
-    for (Row r: rows.collectAsList()) {
-      System.out.println("(" + r.get(0) + ", " + r.get(1) + ") -> prob=" + r.get(2)
-          + ", prediction=" + r.get(3));
-    }
-
-    spark.stop();
-  }
-}
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java
deleted file mode 100644
index 7c24c46d2e..0000000000
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java
+++ /dev/null
@@ -1,93 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.examples.ml;
-
-import java.util.List;
-
-import com.google.common.collect.Lists;
-
-import org.apache.spark.ml.Pipeline;
-import org.apache.spark.ml.PipelineModel;
-import org.apache.spark.ml.PipelineStage;
-import org.apache.spark.ml.classification.LogisticRegression;
-import org.apache.spark.ml.feature.HashingTF;
-import org.apache.spark.ml.feature.Tokenizer;
-import org.apache.spark.sql.Dataset;
-import org.apache.spark.sql.Row;
-import org.apache.spark.sql.SparkSession;
-
-/**
- * A simple text classification pipeline that recognizes "spark" from input text. It uses the Java
- * bean classes {@link LabeledDocument} and {@link Document} defined in the Scala counterpart of
- * this example {@link SimpleTextClassificationPipeline}. Run with
- * <pre>
- * bin/run-example ml.JavaSimpleTextClassificationPipeline
- * </pre>
- */
-public class JavaSimpleTextClassificationPipeline {
-
-  public static void main(String[] args) {
-    SparkSession spark = SparkSession
-      .builder()
-      .appName("JavaSimpleTextClassificationPipeline")
-      .getOrCreate();
-
-    // Prepare training documents, which are labeled.
-    List<LabeledDocument> localTraining = Lists.newArrayList(
-      new LabeledDocument(0L, "a b c d e spark", 1.0),
-      new LabeledDocument(1L, "b d", 0.0),
-      new LabeledDocument(2L, "spark f g h", 1.0),
-      new LabeledDocument(3L, "hadoop mapreduce", 0.0));
-    Dataset<Row> training =
-      spark.createDataFrame(localTraining, LabeledDocument.class);
-
-    // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
-    Tokenizer tokenizer = new Tokenizer()
-      .setInputCol("text")
-      .setOutputCol("words");
-    HashingTF hashingTF = new HashingTF()
-      .setNumFeatures(1000)
-      .setInputCol(tokenizer.getOutputCol())
-      .setOutputCol("features");
-    LogisticRegression lr = new LogisticRegression()
-      .setMaxIter(10)
-      .setRegParam(0.001);
-    Pipeline pipeline = new Pipeline()
-      .setStages(new PipelineStage[] {tokenizer, hashingTF, lr});
-
-    // Fit the pipeline to training documents.
-    PipelineModel model = pipeline.fit(training);
-
-    // Prepare test documents, which are unlabeled.
-    List<Document> localTest = Lists.newArrayList(
-      new Document(4L, "spark i j k"),
-      new Document(5L, "l m n"),
-      new Document(6L, "spark hadoop spark"),
-      new Document(7L, "apache hadoop"));
-    Dataset<Row> test = spark.createDataFrame(localTest, Document.class);
-
-    // Make predictions on test documents.
-    Dataset<Row> predictions = model.transform(test);
-    for (Row r: predictions.select("id", "text", "probability", "prediction").collectAsList()) {
-      System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> prob=" + r.get(2)
-          + ", prediction=" + r.get(3));
-    }
-
-    spark.stop();
-  }
-}
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaStopWordsRemoverExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaStopWordsRemoverExample.java
index def5994429..278cce0842 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaStopWordsRemoverExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaStopWordsRemoverExample.java
@@ -47,7 +47,7 @@ public class JavaStopWordsRemoverExample {
       .setOutputCol("filtered");
 
     List<Row> data = Arrays.asList(
-      RowFactory.create(Arrays.asList("I", "saw", "the", "red", "baloon")),
+      RowFactory.create(Arrays.asList("I", "saw", "the", "red", "balloon")),
       RowFactory.create(Arrays.asList("Mary", "had", "a", "little", "lamb"))
     );
 
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaTokenizerExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaTokenizerExample.java
index 1cc16bb60d..a206cef4c2 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaTokenizerExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaTokenizerExample.java
@@ -57,17 +57,24 @@ public class JavaTokenizerExample {
 
     Tokenizer tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words");
 
-    Dataset<Row> wordsDataFrame = tokenizer.transform(sentenceDataFrame);
-    for (Row r : wordsDataFrame.select("words", "label").takeAsList(3)) {
+    RegexTokenizer regexTokenizer = new RegexTokenizer()
+        .setInputCol("sentence")
+        .setOutputCol("words")
+        .setPattern("\\W");  // alternatively .setPattern("\\w+").setGaps(false);
+
+    Dataset<Row> tokenized = tokenizer.transform(sentenceDataFrame);
+    for (Row r : tokenized.select("words", "label").takeAsList(3)) {
       java.util.List<String> words = r.getList(0);
       for (String word : words) System.out.print(word + " ");
       System.out.println();
     }
 
-    RegexTokenizer regexTokenizer = new RegexTokenizer()
-      .setInputCol("sentence")
-      .setOutputCol("words")
-      .setPattern("\\W");  // alternatively .setPattern("\\w+").setGaps(false);
+    Dataset<Row> regexTokenized = regexTokenizer.transform(sentenceDataFrame);
+    for (Row r : regexTokenized.select("words", "label").takeAsList(3)) {
+      java.util.List<String> words = r.getList(0);
+      for (String word : words) System.out.print(word + " ");
+      System.out.println();
+    }
     // $example off$
     spark.stop();
   }
diff --git a/examples/src/main/python/ml/aft_survival_regression.py b/examples/src/main/python/ml/aft_survival_regression.py
index 060f0171ff..2f0ca995e5 100644
--- a/examples/src/main/python/ml/aft_survival_regression.py
+++ b/examples/src/main/python/ml/aft_survival_regression.py
@@ -32,7 +32,7 @@ Run with:
 if __name__ == "__main__":
     spark = SparkSession \
         .builder \
-        .appName("PythonAFTSurvivalRegressionExample") \
+        .appName("AFTSurvivalRegressionExample") \
         .getOrCreate()
 
     # $example on$
diff --git a/examples/src/main/python/ml/bisecting_k_means_example.py b/examples/src/main/python/ml/bisecting_k_means_example.py
index ee0399ac5e..1263cb5d17 100644
--- a/examples/src/main/python/ml/bisecting_k_means_example.py
+++ b/examples/src/main/python/ml/bisecting_k_means_example.py
@@ -31,7 +31,7 @@ Run with:
 if __name__ == "__main__":
     spark = SparkSession\
         .builder\
-        .appName("PythonBisectingKMeansExample")\
+        .appName("BisectingKMeansExample")\
         .getOrCreate()
 
     # $example on$
diff --git a/examples/src/main/python/ml/cross_validator.py b/examples/src/main/python/ml/cross_validator.py
index a41df6cf94..907eec67a0 100644
--- a/examples/src/main/python/ml/cross_validator.py
+++ b/examples/src/main/python/ml/cross_validator.py
@@ -24,7 +24,7 @@ from pyspark.ml.evaluation import BinaryClassificationEvaluator
 from pyspark.ml.feature import HashingTF, Tokenizer
 from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
 # $example off$
-from pyspark.sql import Row, SparkSession
+from pyspark.sql import SparkSession
 
 """
 A simple example demonstrating model selection using CrossValidator.
@@ -39,6 +39,7 @@ if __name__ == "__main__":
         .builder\
         .appName("CrossValidatorExample")\
         .getOrCreate()
+
     # $example on$
     # Prepare training documents, which are labeled.
     training = spark.createDataFrame([
diff --git a/examples/src/main/python/ml/dataframe_example.py b/examples/src/main/python/ml/dataframe_example.py
index c1818d72fe..109f901012 100644
--- a/examples/src/main/python/ml/dataframe_example.py
+++ b/examples/src/main/python/ml/dataframe_example.py
@@ -34,15 +34,16 @@ if __name__ == "__main__":
     if len(sys.argv) > 2:
         print("Usage: dataframe_example.py <libsvm file>", file=sys.stderr)
         exit(-1)
-    spark = SparkSession\
-        .builder\
-        .appName("DataFrameExample")\
-        .getOrCreate()
-    if len(sys.argv) == 2:
+    elif len(sys.argv) == 2:
         input = sys.argv[1]
     else:
         input = "data/mllib/sample_libsvm_data.txt"
 
+    spark = SparkSession \
+        .builder \
+        .appName("DataFrameExample") \
+        .getOrCreate()
+
     # Load input data
     print("Loading LIBSVM file with UDT from " + input + ".")
     df = spark.read.format("libsvm").load(input).cache()
diff --git a/examples/src/main/python/ml/decision_tree_classification_example.py b/examples/src/main/python/ml/decision_tree_classification_example.py
index 708f1af6cc..d6e2977de0 100644
--- a/examples/src/main/python/ml/decision_tree_classification_example.py
+++ b/examples/src/main/python/ml/decision_tree_classification_example.py
@@ -31,7 +31,7 @@ from pyspark.sql import SparkSession
 if __name__ == "__main__":
     spark = SparkSession\
         .builder\
-        .appName("decision_tree_classification_example")\
+        .appName("DecisionTreeClassificationExample")\
         .getOrCreate()
 
     # $example on$
diff --git a/examples/src/main/python/ml/estimator_transformer_param_example.py b/examples/src/main/python/ml/estimator_transformer_param_example.py
index 3bd3fd30f8..eb21051435 100644
--- a/examples/src/main/python/ml/estimator_transformer_param_example.py
+++ b/examples/src/main/python/ml/estimator_transformer_param_example.py
@@ -18,6 +18,7 @@
 """
 Estimator Transformer Param Example.
 """
+from __future__ import print_function
 
 # $example on$
 from pyspark.ml.linalg import Vectors
@@ -42,7 +43,7 @@ if __name__ == "__main__":
     # Create a LogisticRegression instance. This instance is an Estimator.
     lr = LogisticRegression(maxIter=10, regParam=0.01)
     # Print out the parameters, documentation, and any default values.
-    print "LogisticRegression parameters:\n" + lr.explainParams() + "\n"
+    print("LogisticRegression parameters:\n" + lr.explainParams() + "\n")
 
     # Learn a LogisticRegression model. This uses the parameters stored in lr.
     model1 = lr.fit(training)
@@ -51,8 +52,8 @@ if __name__ == "__main__":
     # we can view the parameters it used during fit().
     # This prints the parameter (name: value) pairs, where names are unique IDs for this
     # LogisticRegression instance.
-    print "Model 1 was fit using parameters: "
-    print model1.extractParamMap()
+    print("Model 1 was fit using parameters: ")
+    print(model1.extractParamMap())
 
     # We may alternatively specify parameters using a Python dictionary as a paramMap
     paramMap = {lr.maxIter: 20}
@@ -67,8 +68,8 @@ if __name__ == "__main__":
     # Now learn a new model using the paramMapCombined parameters.
     # paramMapCombined overrides all parameters set earlier via lr.set* methods.
     model2 = lr.fit(training, paramMapCombined)
-    print "Model 2 was fit using parameters: "
-    print model2.extractParamMap()
+    print("Model 2 was fit using parameters: ")
+    print(model2.extractParamMap())
 
     # Prepare test data
     test = spark.createDataFrame([
@@ -81,9 +82,12 @@ if __name__ == "__main__":
     # Note that model2.transform() outputs a "myProbability" column instead of the usual
     # 'probability' column since we renamed the lr.probabilityCol parameter previously.
     prediction = model2.transform(test)
-    selected = prediction.select("features", "label", "myProbability", "prediction")
-    for row in selected.collect():
-        print row
+    result = prediction.select("features", "label", "myProbability", "prediction") \
+        .collect()
+
+    for row in result:
+        print("features=%s, label=%s -> prob=%s, prediction=%s"
+              % (row.features, row.label, row.myProbability, row.prediction))
     # $example off$
 
     spark.stop()
diff --git a/examples/src/main/python/ml/gaussian_mixture_example.py b/examples/src/main/python/ml/gaussian_mixture_example.py
index 2ca13d68f6..edc258de05 100644
--- a/examples/src/main/python/ml/gaussian_mixture_example.py
+++ b/examples/src/main/python/ml/gaussian_mixture_example.py
@@ -31,7 +31,7 @@ Run with:
 if __name__ == "__main__":
     spark = SparkSession\
         .builder\
-        .appName("PythonGuassianMixtureExample")\
+        .appName("GaussianMixtureExample")\
         .getOrCreate()
 
     # $example on$
diff --git a/examples/src/main/python/ml/gradient_boosted_tree_classifier_example.py b/examples/src/main/python/ml/gradient_boosted_tree_classifier_example.py
index 6c2d7e7b81..c2042fd7b7 100644
--- a/examples/src/main/python/ml/gradient_boosted_tree_classifier_example.py
+++ b/examples/src/main/python/ml/gradient_boosted_tree_classifier_example.py
@@ -31,7 +31,7 @@ from pyspark.sql import SparkSession
 if __name__ == "__main__":
     spark = SparkSession\
         .builder\
-        .appName("gradient_boosted_tree_classifier_example")\
+        .appName("GradientBoostedTreeClassifierExample")\
         .getOrCreate()
 
     # $example on$
diff --git a/examples/src/main/python/ml/gradient_boosted_tree_regressor_example.py b/examples/src/main/python/ml/gradient_boosted_tree_regressor_example.py
index 5dd2272748..cc96c973e4 100644
--- a/examples/src/main/python/ml/gradient_boosted_tree_regressor_example.py
+++ b/examples/src/main/python/ml/gradient_boosted_tree_regressor_example.py
@@ -31,7 +31,7 @@ from pyspark.sql import SparkSession
 if __name__ == "__main__":
     spark = SparkSession\
         .builder\
-        .appName("gradient_boosted_tree_regressor_example")\
+        .appName("GradientBoostedTreeRegressorExample")\
         .getOrCreate()
 
     # $example on$
diff --git a/examples/src/main/python/ml/isotonic_regression_example.py b/examples/src/main/python/ml/isotonic_regression_example.py
index 1e61bd8eff..a41b8ffacb 100644
--- a/examples/src/main/python/ml/isotonic_regression_example.py
+++ b/examples/src/main/python/ml/isotonic_regression_example.py
@@ -21,7 +21,7 @@ Isotonic Regression Example.
 from __future__ import print_function
 
 # $example on$
-from pyspark.ml.regression import IsotonicRegression, IsotonicRegressionModel
+from pyspark.ml.regression import IsotonicRegression
 # $example off$
 from pyspark.sql import SparkSession
 
@@ -30,11 +30,11 @@ An example demonstrating isotonic regression.
 Run with:
   bin/spark-submit examples/src/main/python/ml/isotonic_regression_example.py
 """
-if __name__ == "__main__":
 
+if __name__ == "__main__":
     spark = SparkSession\
         .builder\
-        .appName("PythonIsotonicRegressionExample")\
+        .appName("IsotonicRegressionExample")\
         .getOrCreate()
 
     # $example on$
diff --git a/examples/src/main/python/ml/kmeans_example.py b/examples/src/main/python/ml/kmeans_example.py
index 4b8b7291f9..6846ec4599 100644
--- a/examples/src/main/python/ml/kmeans_example.py
+++ b/examples/src/main/python/ml/kmeans_example.py
@@ -31,12 +31,10 @@ Run with:
 This example requires NumPy (http://www.numpy.org/).
 """
 
-
 if __name__ == "__main__":
-
     spark = SparkSession\
         .builder\
-        .appName("PythonKMeansExample")\
+        .appName("KMeansExample")\
         .getOrCreate()
 
     # $example on$
diff --git a/examples/src/main/python/ml/lda_example.py b/examples/src/main/python/ml/lda_example.py
index 5ce810fccc..2dc1742ff7 100644
--- a/examples/src/main/python/ml/lda_example.py
+++ b/examples/src/main/python/ml/lda_example.py
@@ -23,16 +23,13 @@ from pyspark.ml.clustering import LDA
 # $example off$
 from pyspark.sql import SparkSession
 
-
 """
 An example demonstrating LDA.
 Run with:
   bin/spark-submit examples/src/main/python/ml/lda_example.py
 """
 
-
 if __name__ == "__main__":
-    # Creates a SparkSession
     spark = SparkSession \
         .builder \
         .appName("LDAExample") \
diff --git a/examples/src/main/python/ml/multilayer_perceptron_classification.py b/examples/src/main/python/ml/multilayer_perceptron_classification.py
index aa33bef5a3..2cc38c2855 100644
--- a/examples/src/main/python/ml/multilayer_perceptron_classification.py
+++ b/examples/src/main/python/ml/multilayer_perceptron_classification.py
@@ -31,18 +31,23 @@ if __name__ == "__main__":
     # Load training data
     data = spark.read.format("libsvm")\
         .load("data/mllib/sample_multiclass_classification_data.txt")
+
     # Split the data into train and test
     splits = data.randomSplit([0.6, 0.4], 1234)
     train = splits[0]
     test = splits[1]
+
     # specify layers for the neural network:
     # input layer of size 4 (features), two intermediate of size 5 and 4
     # and output of size 3 (classes)
     layers = [4, 5, 4, 3]
+
     # create the trainer and set its parameters
     trainer = MultilayerPerceptronClassifier(maxIter=100, layers=layers, blockSize=128, seed=1234)
+
     # train the model
     model = trainer.fit(train)
+
     # compute accuracy on the test set
     result = model.transform(test)
     predictionAndLabels = result.select("prediction", "label")
diff --git a/examples/src/main/python/ml/n_gram_example.py b/examples/src/main/python/ml/n_gram_example.py
index 9ac07f2c8e..55263adb46 100644
--- a/examples/src/main/python/ml/n_gram_example.py
+++ b/examples/src/main/python/ml/n_gram_example.py
@@ -34,8 +34,10 @@ if __name__ == "__main__":
         (1, ["I", "wish", "Java", "could", "use", "case", "classes"]),
         (2, ["Logistic", "regression", "models", "are", "neat"])
     ], ["label", "words"])
+
     ngram = NGram(inputCol="words", outputCol="ngrams")
     ngramDataFrame = ngram.transform(wordDataFrame)
+
     for ngrams_label in ngramDataFrame.select("ngrams", "label").take(3):
         print(ngrams_label)
     # $example off$
diff --git a/examples/src/main/python/ml/naive_bayes_example.py b/examples/src/main/python/ml/naive_bayes_example.py
index 8bc32222fe..aa23f298c8 100644
--- a/examples/src/main/python/ml/naive_bayes_example.py
+++ b/examples/src/main/python/ml/naive_bayes_example.py
@@ -26,13 +26,14 @@ from pyspark.sql import SparkSession
 if __name__ == "__main__":
     spark = SparkSession\
         .builder\
-        .appName("naive_bayes_example")\
+        .appName("NaiveBayesExample")\
         .getOrCreate()
 
     # $example on$
     # Load training data
     data = spark.read.format("libsvm") \
         .load("data/mllib/sample_libsvm_data.txt")
+
     # Split the data into train and test
     splits = data.randomSplit([0.6, 0.4], 1234)
     train = splits[0]
@@ -43,6 +44,7 @@ if __name__ == "__main__":
 
     # train the model
     model = nb.fit(train)
+
     # compute accuracy on the test set
     result = model.transform(test)
     predictionAndLabels = result.select("prediction", "label")
diff --git a/examples/src/main/python/ml/one_vs_rest_example.py b/examples/src/main/python/ml/one_vs_rest_example.py
index b82087beba..8e00c25d93 100644
--- a/examples/src/main/python/ml/one_vs_rest_example.py
+++ b/examples/src/main/python/ml/one_vs_rest_example.py
@@ -30,11 +30,10 @@ Run with:
   bin/spark-submit examples/src/main/python/ml/one_vs_rest_example.py
 """
 
-
 if __name__ == "__main__":
     spark = SparkSession \
         .builder \
-        .appName("PythonOneVsRestExample") \
+        .appName("OneVsRestExample") \
         .getOrCreate()
 
     # $example on$
@@ -62,7 +61,7 @@ if __name__ == "__main__":
 
     # compute the classification error on test data.
     accuracy = evaluator.evaluate(predictions)
-    print("Test Error : " + str(1 - accuracy))
+    print("Test Error = %g" % (1.0 - accuracy))
     # $example off$
 
     spark.stop()
diff --git a/examples/src/main/python/ml/onehot_encoder_example.py b/examples/src/main/python/ml/onehot_encoder_example.py
index b9fceef68e..47faf8d202 100644
--- a/examples/src/main/python/ml/onehot_encoder_example.py
+++ b/examples/src/main/python/ml/onehot_encoder_example.py
@@ -41,6 +41,7 @@ if __name__ == "__main__":
     stringIndexer = StringIndexer(inputCol="category", outputCol="categoryIndex")
     model = stringIndexer.fit(df)
     indexed = model.transform(df)
+
     encoder = OneHotEncoder(dropLast=False, inputCol="categoryIndex", outputCol="categoryVec")
     encoded = encoder.transform(indexed)
     encoded.select("id", "categoryVec").show()
diff --git a/examples/src/main/python/ml/pca_example.py b/examples/src/main/python/ml/pca_example.py
index 414629ff88..38746aced0 100644
--- a/examples/src/main/python/ml/pca_example.py
+++ b/examples/src/main/python/ml/pca_example.py
@@ -34,8 +34,10 @@ if __name__ == "__main__":
             (Vectors.dense([2.0, 0.0, 3.0, 4.0, 5.0]),),
             (Vectors.dense([4.0, 0.0, 0.0, 6.0, 7.0]),)]
     df = spark.createDataFrame(data, ["features"])
+
     pca = PCA(k=3, inputCol="features", outputCol="pcaFeatures")
     model = pca.fit(df)
+
     result = model.transform(df).select("pcaFeatures")
     result.show(truncate=False)
     # $example off$
diff --git a/examples/src/main/python/ml/pipeline_example.py b/examples/src/main/python/ml/pipeline_example.py
index bd10cfd7a2..2d0865578a 100644
--- a/examples/src/main/python/ml/pipeline_example.py
+++ b/examples/src/main/python/ml/pipeline_example.py
@@ -38,12 +38,13 @@ if __name__ == "__main__":
         (0L, "a b c d e spark", 1.0),
         (1L, "b d", 0.0),
         (2L, "spark f g h", 1.0),
-        (3L, "hadoop mapreduce", 0.0)], ["id", "text", "label"])
+        (3L, "hadoop mapreduce", 0.0)
+    ], ["id", "text", "label"])
 
     # Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
     tokenizer = Tokenizer(inputCol="text", outputCol="words")
     hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features")
-    lr = LogisticRegression(maxIter=10, regParam=0.01)
+    lr = LogisticRegression(maxIter=10, regParam=0.001)
     pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])
 
     # Fit the pipeline to training documents.
@@ -53,8 +54,9 @@ if __name__ == "__main__":
     test = spark.createDataFrame([
         (4L, "spark i j k"),
         (5L, "l m n"),
-        (6L, "mapreduce spark"),
-        (7L, "apache hadoop")], ["id", "text"])
+        (6L, "spark hadoop spark"),
+        (7L, "apache hadoop")
+    ], ["id", "text"])
 
     # Make predictions on test documents and print columns of interest.
     prediction = model.transform(test)
diff --git a/examples/src/main/python/ml/polynomial_expansion_example.py b/examples/src/main/python/ml/polynomial_expansion_example.py
index b46c1ba2f4..b464ee86b6 100644
--- a/examples/src/main/python/ml/polynomial_expansion_example.py
+++ b/examples/src/main/python/ml/polynomial_expansion_example.py
@@ -30,13 +30,15 @@ if __name__ == "__main__":
         .getOrCreate()
 
     # $example on$
-    df = spark\
-        .createDataFrame([(Vectors.dense([-2.0, 2.3]),),
-                          (Vectors.dense([0.0, 0.0]),),
-                          (Vectors.dense([0.6, -1.1]),)],
-                         ["features"])
+    df = spark.createDataFrame([
+        (Vectors.dense([-2.0, 2.3]),),
+        (Vectors.dense([0.0, 0.0]),),
+        (Vectors.dense([0.6, -1.1]),)
+    ], ["features"])
+
     px = PolynomialExpansion(degree=3, inputCol="features", outputCol="polyFeatures")
     polyDF = px.transform(df)
+
     for expanded in polyDF.select("polyFeatures").take(3):
         print(expanded)
     # $example off$
diff --git a/examples/src/main/python/ml/quantile_discretizer_example.py b/examples/src/main/python/ml/quantile_discretizer_example.py
index 6f422f840a..788a0baffe 100644
--- a/examples/src/main/python/ml/quantile_discretizer_example.py
+++ b/examples/src/main/python/ml/quantile_discretizer_example.py
@@ -22,18 +22,22 @@ from pyspark.ml.feature import QuantileDiscretizer
 # $example off$
 from pyspark.sql import SparkSession
 
-
 if __name__ == "__main__":
-    spark = SparkSession.builder.appName("QuantileDiscretizerExample").getOrCreate()
+    spark = SparkSession\
+        .builder\
+        .appName("QuantileDiscretizerExample")\
+        .getOrCreate()
 
     # $example on$
     data = [(0, 18.0,), (1, 19.0,), (2, 8.0,), (3, 5.0,), (4, 2.2,)]
     df = spark.createDataFrame(data, ["id", "hour"])
     # $example off$
+
     # Output of QuantileDiscretizer for such small datasets can depend on the number of
     # partitions. Here we force a single partition to ensure consistent results.
     # Note this is not necessary for normal use cases
     df = df.repartition(1)
+
     # $example on$
     discretizer = QuantileDiscretizer(numBuckets=3, inputCol="hour", outputCol="result")
 
diff --git a/examples/src/main/python/ml/random_forest_classifier_example.py b/examples/src/main/python/ml/random_forest_classifier_example.py
index eb9ded9af5..4eaa94dd7f 100644
--- a/examples/src/main/python/ml/random_forest_classifier_example.py
+++ b/examples/src/main/python/ml/random_forest_classifier_example.py
@@ -23,7 +23,7 @@ from __future__ import print_function
 # $example on$
 from pyspark.ml import Pipeline
 from pyspark.ml.classification import RandomForestClassifier
-from pyspark.ml.feature import StringIndexer, VectorIndexer
+from pyspark.ml.feature import IndexToString, StringIndexer, VectorIndexer
 from pyspark.ml.evaluation import MulticlassClassificationEvaluator
 # $example off$
 from pyspark.sql import SparkSession
@@ -31,7 +31,7 @@ from pyspark.sql import SparkSession
 if __name__ == "__main__":
     spark = SparkSession\
         .builder\
-        .appName("random_forest_classifier_example")\
+        .appName("RandomForestClassifierExample")\
         .getOrCreate()
 
     # $example on$
@@ -41,6 +41,7 @@ if __name__ == "__main__":
     # Index labels, adding metadata to the label column.
     # Fit on whole dataset to include all labels in index.
     labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data)
+
     # Automatically identify categorical features, and index them.
     # Set maxCategories so features with > 4 distinct values are treated as continuous.
     featureIndexer =\
@@ -52,8 +53,12 @@ if __name__ == "__main__":
     # Train a RandomForest model.
     rf = RandomForestClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures", numTrees=10)
 
+    # Convert indexed labels back to original labels.
+    labelConverter = IndexToString(inputCol="prediction", outputCol="predictedLabel",
+                                   labels=labelIndexer.labels)
+
     # Chain indexers and forest in a Pipeline
-    pipeline = Pipeline(stages=[labelIndexer, featureIndexer, rf])
+    pipeline = Pipeline(stages=[labelIndexer, featureIndexer, rf, labelConverter])
 
     # Train model.  This also runs the indexers.
     model = pipeline.fit(trainingData)
@@ -62,7 +67,7 @@ if __name__ == "__main__":
     predictions = model.transform(testData)
 
     # Select example rows to display.
-    predictions.select("prediction", "indexedLabel", "features").show(5)
+    predictions.select("predictedLabel", "label", "features").show(5)
 
     # Select (prediction, true label) and compute test error
     evaluator = MulticlassClassificationEvaluator(
diff --git a/examples/src/main/python/ml/random_forest_regressor_example.py b/examples/src/main/python/ml/random_forest_regressor_example.py
index 3a793737db..a34edff2ec 100644
--- a/examples/src/main/python/ml/random_forest_regressor_example.py
+++ b/examples/src/main/python/ml/random_forest_regressor_example.py
@@ -31,7 +31,7 @@ from pyspark.sql import SparkSession
 if __name__ == "__main__":
     spark = SparkSession\
         .builder\
-        .appName("random_forest_regressor_example")\
+        .appName("RandomForestRegressorExample")\
         .getOrCreate()
 
     # $example on$
diff --git a/examples/src/main/python/ml/rformula_example.py b/examples/src/main/python/ml/rformula_example.py
index d5df3ce4f5..6629239db2 100644
--- a/examples/src/main/python/ml/rformula_example.py
+++ b/examples/src/main/python/ml/rformula_example.py
@@ -34,10 +34,12 @@ if __name__ == "__main__":
          (8, "CA", 12, 0.0),
          (9, "NZ", 15, 0.0)],
         ["id", "country", "hour", "clicked"])
+
     formula = RFormula(
         formula="clicked ~ country + hour",
         featuresCol="features",
         labelCol="label")
+
     output = formula.fit(dataset).transform(dataset)
     output.select("features", "label").show()
     # $example off$
diff --git a/examples/src/main/python/ml/simple_params_example.py b/examples/src/main/python/ml/simple_params_example.py
deleted file mode 100644
index 2f1eaa6f94..0000000000
--- a/examples/src/main/python/ml/simple_params_example.py
+++ /dev/null
@@ -1,95 +0,0 @@
-#
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements.  See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License.  You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-from __future__ import print_function
-
-import pprint
-import sys
-
-from pyspark.ml.classification import LogisticRegression
-from pyspark.ml.linalg import DenseVector
-from pyspark.sql import Row, SparkSession
-
-"""
-A simple example demonstrating ways to specify parameters for Estimators and Transformers.
-Run with:
-  bin/spark-submit examples/src/main/python/ml/simple_params_example.py
-"""
-
-if __name__ == "__main__":
-    spark = SparkSession \
-        .builder \
-        .appName("SimpleParamsExample") \
-        .getOrCreate()
-
-    # prepare training data.
-    # We create an RDD of LabeledPoints and convert them into a DataFrame.
-    # A LabeledPoint is an Object with two fields named label and features
-    # and Spark SQL identifies these fields and creates the schema appropriately.
-    training = spark.createDataFrame([
-        Row(label=1.0, features=DenseVector([0.0, 1.1, 0.1])),
-        Row(label=0.0, features=DenseVector([2.0, 1.0, -1.0])),
-        Row(label=0.0, features=DenseVector([2.0, 1.3, 1.0])),
-        Row(label=1.0, features=DenseVector([0.0, 1.2, -0.5]))])
-
-    # Create a LogisticRegression instance with maxIter = 10.
-    # This instance is an Estimator.
-    lr = LogisticRegression(maxIter=10)
-    # Print out the parameters, documentation, and any default values.
-    print("LogisticRegression parameters:\n" + lr.explainParams() + "\n")
-
-    # We may also set parameters using setter methods.
-    lr.setRegParam(0.01)
-
-    # Learn a LogisticRegression model.  This uses the parameters stored in lr.
-    model1 = lr.fit(training)
-
-    # Since model1 is a Model (i.e., a Transformer produced by an Estimator),
-    # we can view the parameters it used during fit().
-    # This prints the parameter (name: value) pairs, where names are unique IDs for this
-    # LogisticRegression instance.
-    print("Model 1 was fit using parameters:\n")
-    pprint.pprint(model1.extractParamMap())
-
-    # We may alternatively specify parameters using a parameter map.
-    # paramMap overrides all lr parameters set earlier.
-    paramMap = {lr.maxIter: 20, lr.thresholds: [0.5, 0.5], lr.probabilityCol: "myProbability"}
-
-    # Now learn a new model using the new parameters.
-    model2 = lr.fit(training, paramMap)
-    print("Model 2 was fit using parameters:\n")
-    pprint.pprint(model2.extractParamMap())
-
-    # prepare test data.
-    test = spark.createDataFrame([
-        Row(label=1.0, features=DenseVector([-1.0, 1.5, 1.3])),
-        Row(label=0.0, features=DenseVector([3.0, 2.0, -0.1])),
-        Row(label=0.0, features=DenseVector([0.0, 2.2, -1.5]))])
-
-    # Make predictions on test data using the Transformer.transform() method.
-    # LogisticRegressionModel.transform will only use the 'features' column.
-    # Note that model2.transform() outputs a 'myProbability' column instead of the usual
-    # 'probability' column since we renamed the lr.probabilityCol parameter previously.
-    result = model2.transform(test) \
-        .select("features", "label", "myProbability", "prediction") \
-        .collect()
-
-    for row in result:
-        print("features=%s,label=%s -> prob=%s, prediction=%s"
-              % (row.features, row.label, row.myProbability, row.prediction))
-
-    spark.stop()
diff --git a/examples/src/main/python/ml/simple_text_classification_pipeline.py b/examples/src/main/python/ml/simple_text_classification_pipeline.py
deleted file mode 100644
index b528b59be9..0000000000
--- a/examples/src/main/python/ml/simple_text_classification_pipeline.py
+++ /dev/null
@@ -1,72 +0,0 @@
-#
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements.  See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License.  You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-from __future__ import print_function
-
-from pyspark.ml import Pipeline
-from pyspark.ml.classification import LogisticRegression
-from pyspark.ml.feature import HashingTF, Tokenizer
-from pyspark.sql import Row, SparkSession
-
-
-"""
-A simple text classification pipeline that recognizes "spark" from
-input text. This is to show how to create and configure a Spark ML
-pipeline in Python. Run with:
-
-  bin/spark-submit examples/src/main/python/ml/simple_text_classification_pipeline.py
-"""
-
-
-if __name__ == "__main__":
-    spark = SparkSession\
-        .builder\
-        .appName("SimpleTextClassificationPipeline")\
-        .getOrCreate()
-
-    # Prepare training documents, which are labeled.
-    training = spark.createDataFrame([
-        (0, "a b c d e spark", 1.0),
-        (1, "b d", 0.0),
-        (2, "spark f g h", 1.0),
-        (3, "hadoop mapreduce", 0.0)
-    ], ["id", "text", "label"])
-
-    # Configure an ML pipeline, which consists of tree stages: tokenizer, hashingTF, and lr.
-    tokenizer = Tokenizer(inputCol="text", outputCol="words")
-    hashingTF = HashingTF(numFeatures=1000, inputCol=tokenizer.getOutputCol(), outputCol="features")
-    lr = LogisticRegression(maxIter=10, regParam=0.001)
-    pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])
-
-    # Fit the pipeline to training documents.
-    model = pipeline.fit(training)
-
-    # Prepare test documents, which are unlabeled.
-    test = spark.createDataFrame([
-        (4, "spark i j k"),
-        (5, "l m n"),
-        (6, "spark hadoop spark"),
-        (7, "apache hadoop")
-    ], ["id", "text"])
-
-    # Make predictions on test documents and print columns of interest.
-    prediction = model.transform(test)
-    selected = prediction.select("id", "text", "prediction")
-    for row in selected.collect():
-        print(row)
-
-    spark.stop()
diff --git a/examples/src/main/python/ml/stopwords_remover_example.py b/examples/src/main/python/ml/stopwords_remover_example.py
index 395fdeffc5..8a8392cc1f 100644
--- a/examples/src/main/python/ml/stopwords_remover_example.py
+++ b/examples/src/main/python/ml/stopwords_remover_example.py
@@ -30,7 +30,7 @@ if __name__ == "__main__":
 
     # $example on$
     sentenceData = spark.createDataFrame([
-        (0, ["I", "saw", "the", "red", "baloon"]),
+        (0, ["I", "saw", "the", "red", "balloon"]),
         (1, ["Mary", "had", "a", "little", "lamb"])
     ], ["label", "raw"])
 
diff --git a/examples/src/main/python/ml/string_indexer_example.py b/examples/src/main/python/ml/string_indexer_example.py
index a328e040f5..2255bfb9c1 100644
--- a/examples/src/main/python/ml/string_indexer_example.py
+++ b/examples/src/main/python/ml/string_indexer_example.py
@@ -32,6 +32,7 @@ if __name__ == "__main__":
     df = spark.createDataFrame(
         [(0, "a"), (1, "b"), (2, "c"), (3, "a"), (4, "a"), (5, "c")],
         ["id", "category"])
+
     indexer = StringIndexer(inputCol="category", outputCol="categoryIndex")
     indexed = indexer.fit(df).transform(df)
     indexed.show()
diff --git a/examples/src/main/python/ml/tf_idf_example.py b/examples/src/main/python/ml/tf_idf_example.py
index fb4ad992fb..4ab7eb6964 100644
--- a/examples/src/main/python/ml/tf_idf_example.py
+++ b/examples/src/main/python/ml/tf_idf_example.py
@@ -34,8 +34,10 @@ if __name__ == "__main__":
         (0, "I wish Java could use case classes"),
         (1, "Logistic regression models are neat")
     ], ["label", "sentence"])
+
     tokenizer = Tokenizer(inputCol="sentence", outputCol="words")
     wordsData = tokenizer.transform(sentenceData)
+
     hashingTF = HashingTF(inputCol="words", outputCol="rawFeatures", numFeatures=20)
     featurizedData = hashingTF.transform(wordsData)
     # alternatively, CountVectorizer can also be used to get term frequency vectors
@@ -43,6 +45,7 @@ if __name__ == "__main__":
     idf = IDF(inputCol="rawFeatures", outputCol="features")
     idfModel = idf.fit(featurizedData)
     rescaledData = idfModel.transform(featurizedData)
+
     for features_label in rescaledData.select("features", "label").take(3):
         print(features_label)
     # $example off$
diff --git a/examples/src/main/python/ml/tokenizer_example.py b/examples/src/main/python/ml/tokenizer_example.py
index e61ec920d2..89f5060705 100644
--- a/examples/src/main/python/ml/tokenizer_example.py
+++ b/examples/src/main/python/ml/tokenizer_example.py
@@ -34,12 +34,19 @@ if __name__ == "__main__":
         (1, "I wish Java could use case classes"),
         (2, "Logistic,regression,models,are,neat")
     ], ["label", "sentence"])
+
     tokenizer = Tokenizer(inputCol="sentence", outputCol="words")
-    wordsDataFrame = tokenizer.transform(sentenceDataFrame)
-    for words_label in wordsDataFrame.select("words", "label").take(3):
-        print(words_label)
+
     regexTokenizer = RegexTokenizer(inputCol="sentence", outputCol="words", pattern="\\W")
     # alternatively, pattern="\\w+", gaps(False)
+
+    tokenized = tokenizer.transform(sentenceDataFrame)
+    for words_label in tokenized.select("words", "label").take(3):
+        print(words_label)
+
+    regexTokenized = regexTokenizer.transform(sentenceDataFrame)
+    for words_label in regexTokenized.select("words", "label").take(3):
+        print(words_label)
     # $example off$
 
     spark.stop()
diff --git a/examples/src/main/python/ml/train_validation_split.py b/examples/src/main/python/ml/train_validation_split.py
index 5f5c52aca8..a92b861f83 100644
--- a/examples/src/main/python/ml/train_validation_split.py
+++ b/examples/src/main/python/ml/train_validation_split.py
@@ -35,18 +35,21 @@ if __name__ == "__main__":
         .builder\
         .appName("TrainValidationSplit")\
         .getOrCreate()
+
     # $example on$
     # Prepare training and test data.
     data = spark.read.format("libsvm")\
         .load("data/mllib/sample_linear_regression_data.txt")
-    train, test = data.randomSplit([0.7, 0.3])
-    lr = LinearRegression(maxIter=10, regParam=0.1)
+    train, test = data.randomSplit([0.9, 0.1], seed=12345)
+
+    lr = LinearRegression(maxIter=10)
 
     # We use a ParamGridBuilder to construct a grid of parameters to search over.
     # TrainValidationSplit will try all combinations of values and determine best model using
     # the evaluator.
     paramGrid = ParamGridBuilder()\
         .addGrid(lr.regParam, [0.1, 0.01]) \
+        .addGrid(lr.fitIntercept, [False, True])\
         .addGrid(lr.elasticNetParam, [0.0, 0.5, 1.0])\
         .build()
 
@@ -60,6 +63,7 @@ if __name__ == "__main__":
 
     # Run TrainValidationSplit, and choose the best set of parameters.
     model = tvs.fit(train)
+
     # Make predictions on test data. model is the model with combination of parameters
     # that performed best.
     prediction = model.transform(test)
diff --git a/examples/src/main/python/ml/vector_assembler_example.py b/examples/src/main/python/ml/vector_assembler_example.py
index bbfc316ff2..eac33711ad 100644
--- a/examples/src/main/python/ml/vector_assembler_example.py
+++ b/examples/src/main/python/ml/vector_assembler_example.py
@@ -33,9 +33,11 @@ if __name__ == "__main__":
     dataset = spark.createDataFrame(
         [(0, 18, 1.0, Vectors.dense([0.0, 10.0, 0.5]), 1.0)],
         ["id", "hour", "mobile", "userFeatures", "clicked"])
+
     assembler = VectorAssembler(
         inputCols=["hour", "mobile", "userFeatures"],
         outputCol="features")
+
     output = assembler.transform(dataset)
     print(output.select("features", "clicked").first())
     # $example off$
diff --git a/examples/src/main/python/ml/vector_indexer_example.py b/examples/src/main/python/ml/vector_indexer_example.py
index 9b00e0f841..3912c135be 100644
--- a/examples/src/main/python/ml/vector_indexer_example.py
+++ b/examples/src/main/python/ml/vector_indexer_example.py
@@ -30,6 +30,7 @@ if __name__ == "__main__":
 
     # $example on$
     data = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
+
     indexer = VectorIndexer(inputCol="features", outputCol="indexed", maxCategories=10)
     indexerModel = indexer.fit(data)
 
diff --git a/examples/src/main/python/ml/word2vec_example.py b/examples/src/main/python/ml/word2vec_example.py
index 66500bee15..78a91c92fc 100644
--- a/examples/src/main/python/ml/word2vec_example.py
+++ b/examples/src/main/python/ml/word2vec_example.py
@@ -35,9 +35,11 @@ if __name__ == "__main__":
         ("I wish Java could use case classes".split(" "), ),
         ("Logistic regression models are neat".split(" "), )
     ], ["text"])
+
     # Learn a mapping from words to Vectors.
     word2Vec = Word2Vec(vectorSize=3, minCount=0, inputCol="text", outputCol="result")
     model = word2Vec.fit(documentDF)
+
     result = model.transform(documentDF)
     for feature in result.select("result").take(3):
         print(feature)
diff --git a/examples/src/main/python/streaming/queue_stream.py b/examples/src/main/python/streaming/queue_stream.py
index b3808907f7..bdd2d48519 100644
--- a/examples/src/main/python/streaming/queue_stream.py
+++ b/examples/src/main/python/streaming/queue_stream.py
@@ -22,7 +22,6 @@
  To run this example use
     `$ bin/spark-submit examples/src/main/python/streaming/queue_stream.py
 """
-import sys
 import time
 
 from pyspark import SparkContext
diff --git a/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala b/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala
index a68fd0285f..86eed3867c 100644
--- a/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala
@@ -18,7 +18,6 @@
 // scalastyle:off println
 package org.apache.spark.examples
 
-import org.apache.spark.SparkConf
 import org.apache.spark.sql.SparkSession
 
 /**
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/GaussianMixtureExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/GaussianMixtureExample.scala
index 2c2bf421bc..26095b46f5 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/GaussianMixtureExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/GaussianMixtureExample.scala
@@ -33,8 +33,10 @@ import org.apache.spark.sql.SparkSession
  */
 object GaussianMixtureExample {
   def main(args: Array[String]): Unit = {
-    // Creates a SparkSession
-    val spark = SparkSession.builder.appName(s"${this.getClass.getSimpleName}").getOrCreate()
+    val spark = SparkSession
+        .builder
+        .appName(s"${this.getClass.getSimpleName}")
+        .getOrCreate()
 
     // $example on$
     // Loads data
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/IsotonicRegressionExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/IsotonicRegressionExample.scala
index 7c5d3f2341..a840559d24 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/IsotonicRegressionExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/IsotonicRegressionExample.scala
@@ -33,8 +33,6 @@ import org.apache.spark.sql.SparkSession
 object IsotonicRegressionExample {
 
   def main(args: Array[String]): Unit = {
-
-    // Creates a SparkSession.
     val spark = SparkSession
       .builder
       .appName(s"${this.getClass.getSimpleName}")
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/KMeansExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/KMeansExample.scala
index 2341b36db2..a1d19e138d 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/KMeansExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/KMeansExample.scala
@@ -34,7 +34,6 @@ import org.apache.spark.sql.SparkSession
 object KMeansExample {
 
   def main(args: Array[String]): Unit = {
-    // Creates a SparkSession.
     val spark = SparkSession
       .builder
       .appName(s"${this.getClass.getSimpleName}")
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/ModelSelectionViaTrainValidationSplitExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/ModelSelectionViaTrainValidationSplitExample.scala
index 75fef2922a..1cd2641f9a 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/ModelSelectionViaTrainValidationSplitExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/ModelSelectionViaTrainValidationSplitExample.scala
@@ -46,6 +46,7 @@ object ModelSelectionViaTrainValidationSplitExample {
     val Array(training, test) = data.randomSplit(Array(0.9, 0.1), seed = 12345)
 
     val lr = new LinearRegression()
+        .setMaxIter(10)
 
     // We use a ParamGridBuilder to construct a grid of parameters to search over.
     // TrainValidationSplit will try all combinations of values and determine best model using
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/MultilayerPerceptronClassifierExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/MultilayerPerceptronClassifierExample.scala
index e8a9b32da9..a39e3202ba 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/MultilayerPerceptronClassifierExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/MultilayerPerceptronClassifierExample.scala
@@ -39,27 +39,33 @@ object MultilayerPerceptronClassifierExample {
     // Load the data stored in LIBSVM format as a DataFrame.
     val data = spark.read.format("libsvm")
       .load("data/mllib/sample_multiclass_classification_data.txt")
+
     // Split the data into train and test
     val splits = data.randomSplit(Array(0.6, 0.4), seed = 1234L)
     val train = splits(0)
     val test = splits(1)
+
     // specify layers for the neural network:
     // input layer of size 4 (features), two intermediate of size 5 and 4
     // and output of size 3 (classes)
     val layers = Array[Int](4, 5, 4, 3)
+
     // create the trainer and set its parameters
     val trainer = new MultilayerPerceptronClassifier()
       .setLayers(layers)
       .setBlockSize(128)
       .setSeed(1234L)
       .setMaxIter(100)
+
     // train the model
     val model = trainer.fit(train)
+
     // compute accuracy on the test set
     val result = model.transform(test)
     val predictionAndLabels = result.select("prediction", "label")
     val evaluator = new MulticlassClassificationEvaluator()
       .setMetricName("accuracy")
+
     println("Accuracy: " + evaluator.evaluate(predictionAndLabels))
     // $example off$
 
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/NaiveBayesExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/NaiveBayesExample.scala
index 7089a4bc87..3ae0623c4c 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/NaiveBayesExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/NaiveBayesExample.scala
@@ -30,6 +30,7 @@ object NaiveBayesExample {
       .builder
       .appName("NaiveBayesExample")
       .getOrCreate()
+
     // $example on$
     // Load the data stored in LIBSVM format as a DataFrame.
     val data = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/PipelineExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/PipelineExample.scala
index b16692b1fa..12f8663b9c 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/PipelineExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/PipelineExample.scala
@@ -54,7 +54,7 @@ object PipelineExample {
       .setOutputCol("features")
     val lr = new LogisticRegression()
       .setMaxIter(10)
-      .setRegParam(0.01)
+      .setRegParam(0.001)
     val pipeline = new Pipeline()
       .setStages(Array(tokenizer, hashingTF, lr))
 
@@ -74,7 +74,7 @@ object PipelineExample {
     val test = spark.createDataFrame(Seq(
       (4L, "spark i j k"),
       (5L, "l m n"),
-      (6L, "mapreduce spark"),
+      (6L, "spark hadoop spark"),
       (7L, "apache hadoop")
     )).toDF("id", "text")
 
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/QuantileDiscretizerExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/QuantileDiscretizerExample.scala
index 2f7e217b8f..aedb9e7d3b 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/QuantileDiscretizerExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/QuantileDiscretizerExample.scala
@@ -28,16 +28,16 @@ object QuantileDiscretizerExample {
       .builder
       .appName("QuantileDiscretizerExample")
       .getOrCreate()
-    import spark.implicits._
 
     // $example on$
     val data = Array((0, 18.0), (1, 19.0), (2, 8.0), (3, 5.0), (4, 2.2))
-    var df = spark.createDataFrame(data).toDF("id", "hour")
+    val df = spark.createDataFrame(data).toDF("id", "hour")
     // $example off$
     // Output of QuantileDiscretizer for such small datasets can depend on the number of
     // partitions. Here we force a single partition to ensure consistent results.
     // Note this is not necessary for normal use cases
         .repartition(1)
+
     // $example on$
     val discretizer = new QuantileDiscretizer()
       .setInputCol("hour")
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/RFormulaExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/RFormulaExample.scala
index 9ea4920146..3498fa8a50 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/RFormulaExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/RFormulaExample.scala
@@ -36,10 +36,12 @@ object RFormulaExample {
       (8, "CA", 12, 0.0),
       (9, "NZ", 15, 0.0)
     )).toDF("id", "country", "hour", "clicked")
+
     val formula = new RFormula()
       .setFormula("clicked ~ country + hour")
       .setFeaturesCol("features")
       .setLabelCol("label")
+
     val output = formula.fit(dataset).transform(dataset)
     output.select("features", "label").show()
     // $example off$
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala
deleted file mode 100644
index 29f1f50960..0000000000
--- a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala
+++ /dev/null
@@ -1,104 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-// scalastyle:off println
-package org.apache.spark.examples.ml
-
-import org.apache.spark.ml.classification.LogisticRegression
-import org.apache.spark.ml.feature.LabeledPoint
-import org.apache.spark.ml.linalg.{Vector, Vectors}
-import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.sql.{Row, SparkSession}
-
-/**
- * A simple example demonstrating ways to specify parameters for Estimators and Transformers.
- * Run with
- * {{{
- * bin/run-example ml.SimpleParamsExample
- * }}}
- */
-object SimpleParamsExample {
-
-  def main(args: Array[String]) {
-    val spark = SparkSession
-      .builder
-      .appName("SimpleParamsExample")
-      .getOrCreate()
-    import spark.implicits._
-
-    // Prepare training data.
-    // We use LabeledPoint, which is a case class. Spark SQL can convert RDDs of case classes
-    // into DataFrames, where it uses the case class metadata to infer the schema.
-    val training = spark.createDataFrame(Seq(
-      LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)),
-      LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)),
-      LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)),
-      LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5))))
-
-    // Create a LogisticRegression instance. This instance is an Estimator.
-    val lr = new LogisticRegression()
-    // Print out the parameters, documentation, and any default values.
-    println("LogisticRegression parameters:\n" + lr.explainParams() + "\n")
-
-    // We may set parameters using setter methods.
-    lr.setMaxIter(10)
-      .setRegParam(0.01)
-
-    // Learn a LogisticRegression model. This uses the parameters stored in lr.
-    val model1 = lr.fit(training)
-    // Since model1 is a Model (i.e., a Transformer produced by an Estimator),
-    // we can view the parameters it used during fit().
-    // This prints the parameter (name: value) pairs, where names are unique IDs for this
-    // LogisticRegression instance.
-    println("Model 1 was fit using parameters: " + model1.parent.extractParamMap())
-
-    // We may alternatively specify parameters using a ParamMap,
-    // which supports several methods for specifying parameters.
-    val paramMap = ParamMap(lr.maxIter -> 20)
-    paramMap.put(lr.maxIter, 30) // Specify 1 Param. This overwrites the original maxIter.
-    paramMap.put(lr.regParam -> 0.1, lr.thresholds -> Array(0.5, 0.5)) // Specify multiple Params.
-
-    // One can also combine ParamMaps.
-    val paramMap2 = ParamMap(lr.probabilityCol -> "myProbability") // Change output column name
-    val paramMapCombined = paramMap ++ paramMap2
-
-    // Now learn a new model using the paramMapCombined parameters.
-    // paramMapCombined overrides all parameters set earlier via lr.set* methods.
-    val model2 = lr.fit(training.toDF(), paramMapCombined)
-    println("Model 2 was fit using parameters: " + model2.parent.extractParamMap())
-
-    // Prepare test data.
-    val test = spark.createDataFrame(Seq(
-      LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
-      LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)),
-      LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5))))
-
-    // Make predictions on test data using the Transformer.transform() method.
-    // LogisticRegressionModel.transform will only use the 'features' column.
-    // Note that model2.transform() outputs a 'myProbability' column instead of the usual
-    // 'probability' column since we renamed the lr.probabilityCol parameter previously.
-    model2.transform(test)
-      .select("features", "label", "myProbability", "prediction")
-      .collect()
-      .foreach { case Row(features: Vector, label: Double, prob: Vector, prediction: Double) =>
-        println(s"($features, $label) -> prob=$prob, prediction=$prediction")
-      }
-
-    spark.stop()
-  }
-}
-// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleTextClassificationPipeline.scala b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleTextClassificationPipeline.scala
deleted file mode 100644
index 0b2a058bb6..0000000000
--- a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleTextClassificationPipeline.scala
+++ /dev/null
@@ -1,93 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-// scalastyle:off println
-package org.apache.spark.examples.ml
-
-import scala.beans.BeanInfo
-
-import org.apache.spark.ml.Pipeline
-import org.apache.spark.ml.classification.LogisticRegression
-import org.apache.spark.ml.feature.{HashingTF, Tokenizer}
-import org.apache.spark.ml.linalg.Vector
-import org.apache.spark.sql.{Row, SparkSession}
-
-@BeanInfo
-case class LabeledDocument(id: Long, text: String, label: Double)
-
-@BeanInfo
-case class Document(id: Long, text: String)
-
-/**
- * A simple text classification pipeline that recognizes "spark" from input text. This is to show
- * how to create and configure an ML pipeline. Run with
- * {{{
- * bin/run-example ml.SimpleTextClassificationPipeline
- * }}}
- */
-object SimpleTextClassificationPipeline {
-
-  def main(args: Array[String]) {
-    val spark = SparkSession
-      .builder
-      .appName("SimpleTextClassificationPipeline")
-      .getOrCreate()
-    import spark.implicits._
-
-    // Prepare training documents, which are labeled.
-    val training = spark.createDataFrame(Seq(
-      LabeledDocument(0L, "a b c d e spark", 1.0),
-      LabeledDocument(1L, "b d", 0.0),
-      LabeledDocument(2L, "spark f g h", 1.0),
-      LabeledDocument(3L, "hadoop mapreduce", 0.0)))
-
-    // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
-    val tokenizer = new Tokenizer()
-      .setInputCol("text")
-      .setOutputCol("words")
-    val hashingTF = new HashingTF()
-      .setNumFeatures(1000)
-      .setInputCol(tokenizer.getOutputCol)
-      .setOutputCol("features")
-    val lr = new LogisticRegression()
-      .setMaxIter(10)
-      .setRegParam(0.001)
-    val pipeline = new Pipeline()
-      .setStages(Array(tokenizer, hashingTF, lr))
-
-    // Fit the pipeline to training documents.
-    val model = pipeline.fit(training.toDF())
-
-    // Prepare test documents, which are unlabeled.
-    val test = spark.createDataFrame(Seq(
-      Document(4L, "spark i j k"),
-      Document(5L, "l m n"),
-      Document(6L, "spark hadoop spark"),
-      Document(7L, "apache hadoop")))
-
-    // Make predictions on test documents.
-    model.transform(test.toDF())
-      .select("id", "text", "probability", "prediction")
-      .collect()
-      .foreach { case Row(id: Long, text: String, prob: Vector, prediction: Double) =>
-        println(s"($id, $text) --> prob=$prob, prediction=$prediction")
-      }
-
-    spark.stop()
-  }
-}
-// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/StopWordsRemoverExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/StopWordsRemoverExample.scala
index fb1a43e962..a56de0856d 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/StopWordsRemoverExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/StopWordsRemoverExample.scala
@@ -36,7 +36,7 @@ object StopWordsRemoverExample {
       .setOutputCol("filtered")
 
     val dataSet = spark.createDataFrame(Seq(
-      (0, Seq("I", "saw", "the", "red", "baloon")),
+      (0, Seq("I", "saw", "the", "red", "balloon")),
       (1, Seq("Mary", "had", "a", "little", "lamb"))
     )).toDF("id", "raw")
 
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/TfIdfExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/TfIdfExample.scala
index 33b5daec59..97f6fcce15 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/TfIdfExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/TfIdfExample.scala
@@ -40,13 +40,16 @@ object TfIdfExample {
 
     val tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words")
     val wordsData = tokenizer.transform(sentenceData)
+
     val hashingTF = new HashingTF()
       .setInputCol("words").setOutputCol("rawFeatures").setNumFeatures(20)
+
     val featurizedData = hashingTF.transform(wordsData)
     // alternatively, CountVectorizer can also be used to get term frequency vectors
 
     val idf = new IDF().setInputCol("rawFeatures").setOutputCol("features")
     val idfModel = idf.fit(featurizedData)
+
     val rescaledData = idfModel.transform(featurizedData)
     rescaledData.select("features", "label").take(3).foreach(println)
     // $example off$
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/TokenizerExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/TokenizerExample.scala
index 1c70dc700b..90d0faaf47 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/TokenizerExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/TokenizerExample.scala
@@ -45,6 +45,7 @@ object TokenizerExample {
 
     val tokenized = tokenizer.transform(sentenceDataFrame)
     tokenized.select("words", "label").take(3).foreach(println)
+
     val regexTokenized = regexTokenizer.transform(sentenceDataFrame)
     regexTokenized.select("words", "label").take(3).foreach(println)
     // $example off$
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/Word2VecExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/Word2VecExample.scala
index 9ac5623607..5c8bd19f20 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/Word2VecExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/Word2VecExample.scala
@@ -45,6 +45,7 @@ object Word2VecExample {
       .setVectorSize(3)
       .setMinCount(0)
     val model = word2Vec.fit(documentDF)
+
     val result = model.transform(documentDF)
     result.select("result").take(3).foreach(println)
     // $example off$