[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/java/org/apache/spark/examples/ml/JavaDecisionTreeClassificationExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeClassificationExample.java
index bdb76f004f..a9c6e7f0bf 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeClassificationExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeClassificationExample.java
@@ -90,7 +90,7 @@ public class JavaDecisionTreeClassificationExample {
     MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
       .setLabelCol("indexedLabel")
       .setPredictionCol("prediction")
-      .setMetricName("precision");
+      .setMetricName("accuracy");
     double accuracy = evaluator.evaluate(predictions);
     System.out.println("Test Error = " + (1.0 - accuracy));
 
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeClassifierExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeClassifierExample.java
index 5c2e03eda9..3e9eb998c8 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeClassifierExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeClassifierExample.java
@@ -92,7 +92,7 @@ public class JavaGradientBoostedTreeClassifierExample {
     MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
       .setLabelCol("indexedLabel")
       .setPredictionCol("prediction")
-      .setMetricName("precision");
+      .setMetricName("accuracy");
     double accuracy = evaluator.evaluate(predictions);
     System.out.println("Test Error = " + (1.0 - accuracy));
 
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaMultilayerPerceptronClassifierExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaMultilayerPerceptronClassifierExample.java
index c7d03d8593..0f1d9c2634 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaMultilayerPerceptronClassifierExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaMultilayerPerceptronClassifierExample.java
@@ -57,12 +57,12 @@ public class JavaMultilayerPerceptronClassifierExample {
       .setMaxIter(100);
     // train the model
     MultilayerPerceptronClassificationModel model = trainer.fit(train);
-    // compute precision on the test set
+    // compute accuracy on the test set
     Dataset<Row> result = model.transform(test);
     Dataset<Row> predictionAndLabels = result.select("prediction", "label");
     MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
-      .setMetricName("precision");
-    System.out.println("Precision = " + evaluator.evaluate(predictionAndLabels));
+      .setMetricName("accuracy");
+    System.out.println("Accuracy = " + evaluator.evaluate(predictionAndLabels));
     // $example off$
 
     spark.stop();
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaNaiveBayesExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaNaiveBayesExample.java
index 50a46a5774..3226d5d2fa 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaNaiveBayesExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaNaiveBayesExample.java
@@ -50,12 +50,12 @@ public class JavaNaiveBayesExample {
     NaiveBayes nb = new NaiveBayes();
     // train the model
     NaiveBayesModel model = nb.fit(train);
-    // compute precision on the test set
+    // compute accuracy on the test set
     Dataset<Row> result = model.transform(test);
     Dataset<Row> predictionAndLabels = result.select("prediction", "label");
     MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
-      .setMetricName("precision");
-    System.out.println("Precision = " + evaluator.evaluate(predictionAndLabels));
+      .setMetricName("accuracy");
+    System.out.println("Accuracy = " + evaluator.evaluate(predictionAndLabels));
     // $example off$
 
     spark.stop();
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaOneVsRestExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaOneVsRestExample.java
index 5bf455ebfe..c6a083ddc9 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaOneVsRestExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaOneVsRestExample.java
@@ -71,11 +71,11 @@ public class JavaOneVsRestExample {
 
     // obtain evaluator.
     MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
-            .setMetricName("precision");
+            .setMetricName("accuracy");
 
     // compute the classification error on test data.
-    double precision = evaluator.evaluate(predictions);
-    System.out.println("Test Error : " + (1 - precision));
+    double accuracy = evaluator.evaluate(predictions);
+    System.out.println("Test Error : " + (1 - accuracy));
     // $example off$
 
     spark.stop();
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestClassifierExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestClassifierExample.java
index 14af2fbbbb..da2633e886 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestClassifierExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestClassifierExample.java
@@ -88,7 +88,7 @@ public class JavaRandomForestClassifierExample {
     MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
       .setLabelCol("indexedLabel")
       .setPredictionCol("prediction")
-      .setMetricName("precision");
+      .setMetricName("accuracy");
     double accuracy = evaluator.evaluate(predictions);
     System.out.println("Test Error = " + (1.0 - accuracy));