[SPARK-15771][ML][EXAMPLES] Use 'accuracy' rather than 'precision' in many ML examples

## What changes were proposed in this pull request?
Since [SPARK-15617](https://issues.apache.org/jira/browse/SPARK-15617) deprecated ```precision``` in ```MulticlassClassificationEvaluator```, many ML examples broken.
```python
pyspark.sql.utils.IllegalArgumentException: u'MulticlassClassificationEvaluator_4c3bb1d73d8cc0cedae6 parameter metricName given invalid value precision.'
```
We should use ```accuracy``` to replace ```precision``` in these examples.

## How was this patch tested?
Offline tests.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #13519 from yanboliang/spark-15771.

6 files changed, 12 insertions, 12 deletions

diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeClassificationExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeClassificationExample.scala
index b3103ced91..bc6d327593 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeClassificationExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeClassificationExample.scala
@@ -81,7 +81,7 @@ object DecisionTreeClassificationExample {
     val evaluator = new MulticlassClassificationEvaluator()
       .setLabelCol("indexedLabel")
       .setPredictionCol("prediction")
-      .setMetricName("precision")
+      .setMetricName("accuracy")
     val accuracy = evaluator.evaluate(predictions)
     println("Test Error = " + (1.0 - accuracy))
 
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeClassifierExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeClassifierExample.scala
index 0d1ffbe225..9a39acfbf3 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeClassifierExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeClassifierExample.scala
@@ -83,7 +83,7 @@ object GradientBoostedTreeClassifierExample {
     val evaluator = new MulticlassClassificationEvaluator()
       .setLabelCol("indexedLabel")
       .setPredictionCol("prediction")
-      .setMetricName("precision")
+      .setMetricName("accuracy")
     val accuracy = evaluator.evaluate(predictions)
     println("Test Error = " + (1.0 - accuracy))
 
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 0e780fb7d3..e8a9b32da9 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
@@ -55,12 +55,12 @@ object MultilayerPerceptronClassifierExample {
       .setMaxIter(100)
     // train the model
     val model = trainer.fit(train)
-    // compute precision on the test set
+    // compute accuracy on the test set
     val result = model.transform(test)
     val predictionAndLabels = result.select("prediction", "label")
     val evaluator = new MulticlassClassificationEvaluator()
-      .setMetricName("precision")
-    println("Precision:" + evaluator.evaluate(predictionAndLabels))
+      .setMetricName("accuracy")
+    println("Accuracy: " + evaluator.evaluate(predictionAndLabels))
     // $example off$
 
     spark.stop()
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 90cdebfcb0..a59ba182fc 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
@@ -49,9 +49,9 @@ object NaiveBayesExample {
     val evaluator = new MulticlassClassificationEvaluator()
       .setLabelCol("label")
       .setPredictionCol("prediction")
-      .setMetricName("precision")
-    val precision = evaluator.evaluate(predictions)
-    println("Precision:" + precision)
+      .setMetricName("accuracy")
+    val accuracy = evaluator.evaluate(predictions)
+    println("Accuracy: " + accuracy)
     // $example off$
 
     spark.stop()
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/OneVsRestExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/OneVsRestExample.scala
index 0da8e3137a..acde110683 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/OneVsRestExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/OneVsRestExample.scala
@@ -65,11 +65,11 @@ object OneVsRestExample {
 
     // obtain evaluator.
     val evaluator = new MulticlassClassificationEvaluator()
-      .setMetricName("precision")
+      .setMetricName("accuracy")
 
     // compute the classification error on test data.
-    val precision = evaluator.evaluate(predictions)
-    println(s"Test Error : ${1 - precision}")
+    val accuracy = evaluator.evaluate(predictions)
+    println(s"Test Error : ${1 - accuracy}")
     // $example off$
 
     spark.stop()
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestClassifierExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestClassifierExample.scala
index cccc4a6ea2..5eafda8ce4 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestClassifierExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestClassifierExample.scala
@@ -83,7 +83,7 @@ object RandomForestClassifierExample {
     val evaluator = new MulticlassClassificationEvaluator()
       .setLabelCol("indexedLabel")
       .setPredictionCol("prediction")
-      .setMetricName("precision")
+      .setMetricName("accuracy")
     val accuracy = evaluator.evaluate(predictions)
     println("Test Error = " + (1.0 - accuracy))