[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/python/ml/decision_tree_classification_example.py b/examples/src/main/python/ml/decision_tree_classification_example.py
index 9b40b701ec..708f1af6cc 100644
--- a/examples/src/main/python/ml/decision_tree_classification_example.py
+++ b/examples/src/main/python/ml/decision_tree_classification_example.py
@@ -66,7 +66,7 @@ if __name__ == "__main__":
 
     # Select (prediction, true label) and compute test error
     evaluator = MulticlassClassificationEvaluator(
-        labelCol="indexedLabel", predictionCol="prediction", metricName="precision")
+        labelCol="indexedLabel", predictionCol="prediction", metricName="accuracy")
     accuracy = evaluator.evaluate(predictions)
     print("Test Error = %g " % (1.0 - accuracy))
 
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 50026d7b7e..6c2d7e7b81 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
@@ -66,7 +66,7 @@ if __name__ == "__main__":
 
     # Select (prediction, true label) and compute test error
     evaluator = MulticlassClassificationEvaluator(
-        labelCol="indexedLabel", predictionCol="prediction", metricName="precision")
+        labelCol="indexedLabel", predictionCol="prediction", metricName="accuracy")
     accuracy = evaluator.evaluate(predictions)
     print("Test Error = %g" % (1.0 - accuracy))
 
diff --git a/examples/src/main/python/ml/multilayer_perceptron_classification.py b/examples/src/main/python/ml/multilayer_perceptron_classification.py
index 8bededc14d..aa33bef5a3 100644
--- a/examples/src/main/python/ml/multilayer_perceptron_classification.py
+++ b/examples/src/main/python/ml/multilayer_perceptron_classification.py
@@ -43,11 +43,11 @@ if __name__ == "__main__":
     trainer = MultilayerPerceptronClassifier(maxIter=100, layers=layers, blockSize=128, seed=1234)
     # train the model
     model = trainer.fit(train)
-    # compute precision on the test set
+    # compute accuracy on the test set
     result = model.transform(test)
     predictionAndLabels = result.select("prediction", "label")
-    evaluator = MulticlassClassificationEvaluator(metricName="precision")
-    print("Precision:" + str(evaluator.evaluate(predictionAndLabels)))
+    evaluator = MulticlassClassificationEvaluator(metricName="accuracy")
+    print("Accuracy: " + str(evaluator.evaluate(predictionAndLabels)))
     # $example off$
 
     spark.stop()
diff --git a/examples/src/main/python/ml/naive_bayes_example.py b/examples/src/main/python/ml/naive_bayes_example.py
index 89255a2bae..8bc32222fe 100644
--- a/examples/src/main/python/ml/naive_bayes_example.py
+++ b/examples/src/main/python/ml/naive_bayes_example.py
@@ -43,11 +43,11 @@ if __name__ == "__main__":
 
     # train the model
     model = nb.fit(train)
-    # compute precision on the test set
+    # compute accuracy on the test set
     result = model.transform(test)
     predictionAndLabels = result.select("prediction", "label")
-    evaluator = MulticlassClassificationEvaluator(metricName="precision")
-    print("Precision:" + str(evaluator.evaluate(predictionAndLabels)))
+    evaluator = MulticlassClassificationEvaluator(metricName="accuracy")
+    print("Accuracy: " + str(evaluator.evaluate(predictionAndLabels)))
     # $example off$
 
     spark.stop()
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 971156d0dd..b82087beba 100644
--- a/examples/src/main/python/ml/one_vs_rest_example.py
+++ b/examples/src/main/python/ml/one_vs_rest_example.py
@@ -58,11 +58,11 @@ if __name__ == "__main__":
     predictions = ovrModel.transform(test)
 
     # obtain evaluator.
-    evaluator = MulticlassClassificationEvaluator(metricName="precision")
+    evaluator = MulticlassClassificationEvaluator(metricName="accuracy")
 
     # compute the classification error on test data.
-    precision = evaluator.evaluate(predictions)
-    print("Test Error : " + str(1 - precision))
+    accuracy = evaluator.evaluate(predictions)
+    print("Test Error : " + str(1 - accuracy))
     # $example off$
 
     spark.stop()
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 c618eaf60c..a7fc765318 100644
--- a/examples/src/main/python/ml/random_forest_classifier_example.py
+++ b/examples/src/main/python/ml/random_forest_classifier_example.py
@@ -66,7 +66,7 @@ if __name__ == "__main__":
 
     # Select (prediction, true label) and compute test error
     evaluator = MulticlassClassificationEvaluator(
-        labelCol="indexedLabel", predictionCol="prediction", metricName="precision")
+        labelCol="indexedLabel", predictionCol="prediction", metricName="accuracy")
     accuracy = evaluator.evaluate(predictions)
     print("Test Error = %g" % (1.0 - accuracy))