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authorYuhao Yang <hhbyyh@gmail.com>2016-04-13 13:58:35 -0700
committerJoseph K. Bradley <joseph@databricks.com>2016-04-13 13:58:35 -0700
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treef27ffcec85b7e7661eeefd6b02b2e8aa8df01cf5 /examples/src/main/scala
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[SPARK-13089][ML] [Doc] spark.ml Naive Bayes user guide and examples
jira: https://issues.apache.org/jira/browse/SPARK-13089 Add section in ml-classification.md for NaiveBayes DataFrame-based API, plus example code (using include_example to clip code from examples/ folder files). Author: Yuhao Yang <hhbyyh@gmail.com> Closes #11015 from hhbyyh/naiveBayesDoc.
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+/*
+ * 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.{SparkConf, SparkContext}
+// $example on$
+import org.apache.spark.ml.classification.{NaiveBayes}
+import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
+// $example off$
+import org.apache.spark.sql.SQLContext
+
+object NaiveBayesExample {
+ def main(args: Array[String]): Unit = {
+ val conf = new SparkConf().setAppName("NaiveBayesExample")
+ val sc = new SparkContext(conf)
+ val sqlContext = new SQLContext(sc)
+ // $example on$
+ // Load the data stored in LIBSVM format as a DataFrame.
+ val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
+
+ // Split the data into training and test sets (30% held out for testing)
+ val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
+
+ // Train a NaiveBayes model.
+ val model = new NaiveBayes()
+ .fit(trainingData)
+
+ // Select example rows to display.
+ val predictions = model.transform(testData)
+ predictions.show()
+
+ // Select (prediction, true label) and compute test error
+ val evaluator = new MulticlassClassificationEvaluator()
+ .setLabelCol("label")
+ .setPredictionCol("prediction")
+ .setMetricName("precision")
+ val precision = evaluator.evaluate(predictions)
+ println("Precision:" + precision)
+ // $example off$
+ }
+}
+// scalastyle:on println