所用数据源，请参考本人博客http://www.cnblogs.com/wwxbi/p/6063613.html

1.导入包

import org.apache.spark.sql.SparkSession

import org.apache.spark.sql.Dataset

import org.apache.spark.sql.Row

import org.apache.spark.sql.DataFrame

import org.apache.spark.sql.Column

import org.apache.spark.sql.DataFrameReader

import org.apache.spark.rdd.RDD

import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder

import org.apache.spark.sql.Encoder

import org.apache.spark.sql.DataFrameStatFunctions

import org.apache.spark.sql.functions._

import org.apache.spark.ml.Pipeline

import org.apache.spark.ml.classification.DecisionTreeClassificationModel

import org.apache.spark.ml.classification.DecisionTreeClassifier

import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator

import org.apache.spark.ml.feature.VectorAssembler

import org.apache.spark.ml.feature.StringIndexer

import org.apache.spark.ml.feature.IndexToString

import org.apache.spark.ml.feature.VectorIndexer

import org.apache.spark.ml.feature.VectorSlicer

2.加载数据源

val spark = SparkSession.builder().appName("Spark decision tree classifier").config("spark.some.config.option", "some-value").getOrCreate()

// For implicit conversions like converting RDDs to DataFrames

import spark.implicits._

// 这里仅仅是示例数据，数据源，请参考本人博客http://www.cnblogs.com/wwxbi/p/6063613.html

val dataList: List[(Double, String, Double, Double, String, Double, Double, Double, Double)] = List(

  (0, "male", 37, 10, "no", 3, 18, 7, 4),

  (0, "female", 27, 4, "no", 4, 14, 6, 4),

  (0, "female", 32, 15, "yes", 1, 12, 1, 4),

  (0, "male", 57, 15, "yes", 5, 18, 6, 5),

  (0, "male", 22, 0.75, "no", 2, 17, 6, 3),

  (0, "female", 32, 1.5, "no", 2, 17, 5, 5))

val data = dataList.toDF("affairs", "gender", "age", "yearsmarried", "children", "religiousness", "education", "occupation", "rating")

data.createOrReplaceTempView("data")

// 字符类型转换成数值

val labelWhere = "case when affairs=0 then 0 else cast(1 as double) end as label"

val genderWhere = "case when gender='female' then 0 else cast(1 as double) end as gender"

val childrenWhere = "case when children='no' then 0 else cast(1 as double) end as children"

val dataLabelDF = spark.sql(s"select $labelWhere, $genderWhere,age,yearsmarried,$childrenWhere,religiousness,education,occupation,rating from data")

 3.创建决策树模型

val featuresArray = Array("gender", "age", "yearsmarried", "children", "religiousness", "education", "occupation", "rating")

// 字段转换成特征向量

val assembler = new VectorAssembler().setInputCols(featuresArray).setOutputCol("features")

val vecDF: DataFrame = assembler.transform(dataLabelDF)

vecDF.show(10, truncate = false)

// 索引标签，将元数据添加到标签列中

val labelIndexer = new StringIndexer().setInputCol("label").setOutputCol("indexedLabel").fit(vecDF)

labelIndexer.transform(vecDF).show(10, truncate = false)

// 自动识别分类的特征，并对它们进行索引

// 具有大于5个不同的值的特征被视为连续。

val featureIndexer = new VectorIndexer().setInputCol("features").setOutputCol("indexedFeatures").setMaxCategories(5).fit(vecDF)

featureIndexer.transform(vecDF).show(10, truncate = false)

// 将数据分为训练和测试集（30%进行测试）

val Array(trainingData, testData) = vecDF.randomSplit(Array(0.7, 0.3))

// 训练决策树模型

val dt = new DecisionTreeClassifier()

.setLabelCol("indexedLabel")

.setFeaturesCol("indexedFeatures")

.setImpurity("entropy") // 不纯度

.setMaxBins(100) // 离散化"连续特征"的最大划分数

.setMaxDepth(5) // 树的最大深度

.setMinInfoGain(0.01) //一个节点分裂的最小信息增益，值为[0,1]

.setMinInstancesPerNode(10) //每个节点包含的最小样本数

.setSeed(123456)

// 将索引标签转换回原始标签

val labelConverter = new IndexToString().setInputCol("prediction").setOutputCol("predictedLabel").setLabels(labelIndexer.labels)

// Chain indexers and tree in a Pipeline.

val pipeline = new Pipeline().setStages(Array(labelIndexer, featureIndexer, dt, labelConverter))

// Train model. This also runs the indexers.

val model = pipeline.fit(trainingData)

// 作出预测

val predictions = model.transform(testData)

// 选择几个示例行展示

predictions.select("predictedLabel", "label", "features").show(10, truncate = false)

// 选择（预测标签，实际标签），并计算测试误差。

val evaluator = new MulticlassClassificationEvaluator().setLabelCol("indexedLabel").setPredictionCol("prediction").setMetricName("accuracy")

val accuracy = evaluator.evaluate(predictions)

println("Test Error = " + (1.0 - accuracy))

// 这里的stages(2)中的“2”对应pipeline中的“dt”，将model强制转换为DecisionTreeClassificationModel类型

val treeModel = model.stages(2).asInstanceOf[DecisionTreeClassificationModel]

treeModel.getLabelCol

treeModel.getFeaturesCol

treeModel.featureImportances

treeModel.getPredictionCol

treeModel.getProbabilityCol

treeModel.numClasses

treeModel.numFeatures

treeModel.depth

treeModel.numNodes

treeModel.getImpurity

treeModel.getMaxBins

treeModel.getMaxDepth

treeModel.getMaxMemoryInMB

treeModel.getMinInfoGain

treeModel.getMinInstancesPerNode

println("Learned classification tree model:\n" + treeModel.toDebugString)

 

4.代码执行结果

val data = dataList.toDF("affairs", "gender", "age", "yearsmarried", "children", "religiousness", "education", "occupation", "rating")

data.show(10, truncate = false)

+-------+------+----+------------+--------+-------------+---------+----------+------+

|affairs|gender|age |yearsmarried|children|religiousness|education|occupation|rating|

+-------+------+----+------------+--------+-------------+---------+----------+------+

|0.0    |male  |37.0|10.0        |no      |3.0          |18.0     |7.0       |4.0   |

|0.0    |female|27.0|4.0         |no      |4.0          |14.0     |6.0       |4.0   |

|0.0    |female|32.0|15.0        |yes     |1.0          |12.0     |1.0       |4.0   |

|0.0    |male  |57.0|15.0        |yes     |5.0          |18.0     |6.0       |5.0   |

|0.0    |male  |22.0|0.75        |no      |2.0          |17.0     |6.0       |3.0   |

|0.0    |female|32.0|1.5         |no      |2.0          |17.0     |5.0       |5.0   |

|0.0    |female|22.0|0.75        |no      |2.0          |12.0     |1.0       |3.0   |

|0.0    |male  |57.0|15.0        |yes     |2.0          |14.0     |4.0       |4.0   |

|0.0    |female|32.0|15.0        |yes     |4.0          |16.0     |1.0       |2.0   |

|0.0    |male  |22.0|1.5         |no      |4.0          |14.0     |4.0       |5.0   |

+-------+------+----+------------+--------+-------------+---------+----------+------+

only showing top 10 rows

data.createOrReplaceTempView("data")

// 字符类型转换成数值

val labelWhere = "case when affairs=0 then 0 else cast(1 as double) end as label"

val genderWhere = "case when gender='female' then 0 else cast(1 as double) end as gender"

val childrenWhere = "case when children='no' then 0 else cast(1 as double) end as children"

val dataLabelDF = spark.sql(s"select $labelWhere, $genderWhere,age,yearsmarried,$childrenWhere,religiousness,education,occupation,rating from data")

dataLabelDF.show(10, truncate = false)

+-----+------+----+------------+--------+-------------+---------+----------+------+

|label|gender|age |yearsmarried|children|religiousness|education|occupation|rating|

+-----+------+----+------------+--------+-------------+---------+----------+------+

|0.0  |1.0   |37.0|10.0        |0.0     |3.0          |18.0     |7.0       |4.0   |

|0.0  |0.0   |27.0|4.0         |0.0     |4.0          |14.0     |6.0       |4.0   |

|0.0  |0.0   |32.0|15.0        |1.0     |1.0          |12.0     |1.0       |4.0   |

|0.0  |1.0   |57.0|15.0        |1.0     |5.0          |18.0     |6.0       |5.0   |

|0.0  |1.0   |22.0|0.75        |0.0     |2.0          |17.0     |6.0       |3.0   |

|0.0  |0.0   |32.0|1.5         |0.0     |2.0          |17.0     |5.0       |5.0   |

|0.0  |0.0   |22.0|0.75        |0.0     |2.0          |12.0     |1.0       |3.0   |

|0.0  |1.0   |57.0|15.0        |1.0     |2.0          |14.0     |4.0       |4.0   |

|0.0  |0.0   |32.0|15.0        |1.0     |4.0          |16.0     |1.0       |2.0   |

|0.0  |1.0   |22.0|1.5         |0.0     |4.0          |14.0     |4.0       |5.0   |

+-----+------+----+------------+--------+-------------+---------+----------+------+

only showing top 10 rows

val featuresArray = Array("gender", "age", "yearsmarried", "children", "religiousness", "education", "occupation", "rating")

// 字段转换成特征向量

val assembler = new VectorAssembler().setInputCols(featuresArray).setOutputCol("features")

val vecDF: DataFrame = assembler.transform(dataLabelDF)

vecDF.show(10, truncate = false)

+-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+

|label|gender|age |yearsmarried|children|religiousness|education|occupation|rating|features                            |

+-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+

|0.0  |1.0   |37.0|10.0        |0.0     |3.0          |18.0     |7.0       |4.0   |[1.0,37.0,10.0,0.0,3.0,18.0,7.0,4.0]|

|0.0  |0.0   |27.0|4.0         |0.0     |4.0          |14.0     |6.0       |4.0   |[0.0,27.0,4.0,0.0,4.0,14.0,6.0,4.0] |

|0.0  |0.0   |32.0|15.0        |1.0     |1.0          |12.0     |1.0       |4.0   |[0.0,32.0,15.0,1.0,1.0,12.0,1.0,4.0]|

|0.0  |1.0   |57.0|15.0        |1.0     |5.0          |18.0     |6.0       |5.0   |[1.0,57.0,15.0,1.0,5.0,18.0,6.0,5.0]|

|0.0  |1.0   |22.0|0.75        |0.0     |2.0          |17.0     |6.0       |3.0   |[1.0,22.0,0.75,0.0,2.0,17.0,6.0,3.0]|

|0.0  |0.0   |32.0|1.5         |0.0     |2.0          |17.0     |5.0       |5.0   |[0.0,32.0,1.5,0.0,2.0,17.0,5.0,5.0] |

|0.0  |0.0   |22.0|0.75        |0.0     |2.0          |12.0     |1.0       |3.0   |[0.0,22.0,0.75,0.0,2.0,12.0,1.0,3.0]|

|0.0  |1.0   |57.0|15.0        |1.0     |2.0          |14.0     |4.0       |4.0   |[1.0,57.0,15.0,1.0,2.0,14.0,4.0,4.0]|

|0.0  |0.0   |32.0|15.0        |1.0     |4.0          |16.0     |1.0       |2.0   |[0.0,32.0,15.0,1.0,4.0,16.0,1.0,2.0]|

|0.0  |1.0   |22.0|1.5         |0.0     |4.0          |14.0     |4.0       |5.0   |[1.0,22.0,1.5,0.0,4.0,14.0,4.0,5.0] |

+-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+

only showing top 10 rows

// 索引标签，将元数据添加到标签列中

val labelIndexer = new StringIndexer().setInputCol("label").setOutputCol("indexedLabel").fit(vecDF)

labelIndexer.transform(vecDF).show(10, truncate = false)

+-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+------------+

|label|gender|age |yearsmarried|children|religiousness|education|occupation|rating|features                            |indexedLabel|

+-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+------------+

|0.0  |1.0   |37.0|10.0        |0.0     |3.0          |18.0     |7.0       |4.0   |[1.0,37.0,10.0,0.0,3.0,18.0,7.0,4.0]|0.0         |

|0.0  |0.0   |27.0|4.0         |0.0     |4.0          |14.0     |6.0       |4.0   |[0.0,27.0,4.0,0.0,4.0,14.0,6.0,4.0] |0.0         |

|0.0  |0.0   |32.0|15.0        |1.0     |1.0          |12.0     |1.0       |4.0   |[0.0,32.0,15.0,1.0,1.0,12.0,1.0,4.0]|0.0         |

|0.0  |1.0   |57.0|15.0        |1.0     |5.0          |18.0     |6.0       |5.0   |[1.0,57.0,15.0,1.0,5.0,18.0,6.0,5.0]|0.0         |

|0.0  |1.0   |22.0|0.75        |0.0     |2.0          |17.0     |6.0       |3.0   |[1.0,22.0,0.75,0.0,2.0,17.0,6.0,3.0]|0.0         |

|0.0  |0.0   |32.0|1.5         |0.0     |2.0          |17.0     |5.0       |5.0   |[0.0,32.0,1.5,0.0,2.0,17.0,5.0,5.0] |0.0         |

|0.0  |0.0   |22.0|0.75        |0.0     |2.0          |12.0     |1.0       |3.0   |[0.0,22.0,0.75,0.0,2.0,12.0,1.0,3.0]|0.0         |

|0.0  |1.0   |57.0|15.0        |1.0     |2.0          |14.0     |4.0       |4.0   |[1.0,57.0,15.0,1.0,2.0,14.0,4.0,4.0]|0.0         |

|0.0  |0.0   |32.0|15.0        |1.0     |4.0          |16.0     |1.0       |2.0   |[0.0,32.0,15.0,1.0,4.0,16.0,1.0,2.0]|0.0         |

|0.0  |1.0   |22.0|1.5         |0.0     |4.0          |14.0     |4.0       |5.0   |[1.0,22.0,1.5,0.0,4.0,14.0,4.0,5.0] |0.0         |

+-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+------------+

only showing top 10 rows

// 自动识别分类的特征，并对它们进行索引

// 具有大于5个不同的值的特征被视为连续。

val featureIndexer = new VectorIndexer().setInputCol("features").setOutputCol("indexedFeatures").setMaxCategories(5).fit(vecDF)

featureIndexer.transform(vecDF).show(10, truncate = false)

featureIndexer.transform(vecDF).show(10, truncate = false)

+-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+------------------------------------+

|label|gender|age |yearsmarried|children|religiousness|education|occupation|rating|features                            |indexedFeatures                     |

+-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+------------------------------------+

|0.0  |1.0   |37.0|10.0        |0.0     |3.0          |18.0     |7.0       |4.0   |[1.0,37.0,10.0,0.0,3.0,18.0,7.0,4.0]|[1.0,37.0,10.0,0.0,2.0,18.0,7.0,3.0]|

|0.0  |0.0   |27.0|4.0         |0.0     |4.0          |14.0     |6.0       |4.0   |[0.0,27.0,4.0,0.0,4.0,14.0,6.0,4.0] |[0.0,27.0,4.0,0.0,3.0,14.0,6.0,3.0] |

|0.0  |0.0   |32.0|15.0        |1.0     |1.0          |12.0     |1.0       |4.0   |[0.0,32.0,15.0,1.0,1.0,12.0,1.0,4.0]|[0.0,32.0,15.0,1.0,0.0,12.0,1.0,3.0]|

|0.0  |1.0   |57.0|15.0        |1.0     |5.0          |18.0     |6.0       |5.0   |[1.0,57.0,15.0,1.0,5.0,18.0,6.0,5.0]|[1.0,57.0,15.0,1.0,4.0,18.0,6.0,4.0]|

|0.0  |1.0   |22.0|0.75        |0.0     |2.0          |17.0     |6.0       |3.0   |[1.0,22.0,0.75,0.0,2.0,17.0,6.0,3.0]|[1.0,22.0,0.75,0.0,1.0,17.0,6.0,2.0]|

|0.0  |0.0   |32.0|1.5         |0.0     |2.0          |17.0     |5.0       |5.0   |[0.0,32.0,1.5,0.0,2.0,17.0,5.0,5.0] |[0.0,32.0,1.5,0.0,1.0,17.0,5.0,4.0] |

|0.0  |0.0   |22.0|0.75        |0.0     |2.0          |12.0     |1.0       |3.0   |[0.0,22.0,0.75,0.0,2.0,12.0,1.0,3.0]|[0.0,22.0,0.75,0.0,1.0,12.0,1.0,2.0]|

|0.0  |1.0   |57.0|15.0        |1.0     |2.0          |14.0     |4.0       |4.0   |[1.0,57.0,15.0,1.0,2.0,14.0,4.0,4.0]|[1.0,57.0,15.0,1.0,1.0,14.0,4.0,3.0]|

|0.0  |0.0   |32.0|15.0        |1.0     |4.0          |16.0     |1.0       |2.0   |[0.0,32.0,15.0,1.0,4.0,16.0,1.0,2.0]|[0.0,32.0,15.0,1.0,3.0,16.0,1.0,1.0]|

|0.0  |1.0   |22.0|1.5         |0.0     |4.0          |14.0     |4.0       |5.0   |[1.0,22.0,1.5,0.0,4.0,14.0,4.0,5.0] |[1.0,22.0,1.5,0.0,3.0,14.0,4.0,4.0] |

+-----+------+----+------------+--------+-------------+---------+----------+------+------------------------------------+------------------------------------+

only showing top 10 rows

// 将数据分为训练和测试集（30%进行测试）

val Array(trainingData, testData) = vecDF.randomSplit(Array(0.7, 0.3))

// 训练决策树模型

val dt = new DecisionTreeClassifier().setLabelCol("indexedLabel").setFeaturesCol("indexedFeatures").setImpurity("entropy").setMaxBins(100).setMaxDepth(5).setMinInfoGain(0.01).setMinInstancesPerNode(10).setSeed(123456)

//.setLabelCol("indexedLabel")

//.setFeaturesCol("indexedFeatures")

//.setImpurity("entropy") // 不纯度

//.setMaxBins(100) // 离散化"连续特征"的最大划分数

//.setMaxDepth(5) // 树的最大深度

//.setMinInfoGain(0.01) //一个节点分裂的最小信息增益，值为[0,1]

//.setMinInstancesPerNode(10) //每个节点包含的最小样本数

//.setSeed(123456)

// 将索引标签转换回原始标签

val labelConverter = new IndexToString().setInputCol("prediction").setOutputCol("predictedLabel").setLabels(labelIndexer.labels)

// Chain indexers and tree in a Pipeline.

val pipeline = new Pipeline().setStages(Array(labelIndexer, featureIndexer, dt, labelConverter))

// Train model. This also runs the indexers.

val model = pipeline.fit(trainingData)

// 作出预测

val predictions = model.transform(testData)

// 选择几个示例行展示

predictions.select("predictedLabel", "label", "features").show(10, truncate = false)

+--------------+-----+-------------------------------------+

|predictedLabel|label|features                             |

+--------------+-----+-------------------------------------+

|0.0           |0.0  |[0.0,22.0,0.125,0.0,2.0,14.0,4.0,5.0]|

|0.0           |0.0  |[0.0,22.0,0.417,0.0,1.0,17.0,6.0,4.0]|

|0.0           |0.0  |[0.0,22.0,0.75,0.0,2.0,18.0,6.0,5.0] |

|0.0           |0.0  |[0.0,22.0,0.75,0.0,3.0,16.0,1.0,5.0] |

|0.0           |0.0  |[0.0,22.0,0.75,0.0,4.0,16.0,1.0,5.0] |

|0.0           |0.0  |[0.0,22.0,1.5,0.0,1.0,14.0,1.0,5.0]  |

|0.0           |0.0  |[0.0,22.0,1.5,0.0,2.0,14.0,1.0,5.0]  |

|0.0           |0.0  |[0.0,22.0,1.5,0.0,2.0,16.0,5.0,5.0]  |

|0.0           |0.0  |[0.0,22.0,1.5,0.0,2.0,16.0,5.0,5.0]  |

|0.0           |0.0  |[0.0,22.0,1.5,0.0,2.0,17.0,5.0,4.0]  |

+--------------+-----+-------------------------------------+

// 选择（预测标签，实际标签），并计算测试误差。

val evaluator = new MulticlassClassificationEvaluator().setLabelCol("indexedLabel").setPredictionCol("prediction").setMetricName("accuracy")

val accuracy = evaluator.evaluate(predictions)

accuracy: Double = 0.6972972972972973

println("Test Error = " + (1.0 - accuracy))

Test Error = 0.3027027027027027

// 这里的stages(2)中的“2”对应pipeline中的“dt”，将model强制转换为DecisionTreeClassificationModel类型

val treeModel = model.stages(2).asInstanceOf[DecisionTreeClassificationModel]

DecisionTreeClassificationModel (uid=dtc_b950f91d35f8) of depth 5 with 43 nodes

treeModel.getLabelCol

String = indexedLabel

treeModel.getFeaturesCol

String = indexedFeatures

treeModel.featureImportances

Vector = (8,[0,1,2,4,5,6,7],[0.012972759843658999,0.1075317063921102,0.11654682273543511,0.17869552275855793,0.07532637852021348,0.27109893303920024,0.237827

876710824])

treeModel.getPredictionCol

String = prediction

treeModel.getProbabilityCol

String = probability

treeModel.numClasses

Int = 2

treeModel.numFeatures

Int = 8

treeModel.depth

Int = 5

treeModel.numNodes

Int = 43

treeModel.getImpurity

String = entropy

treeModel.getMaxBins

Int = 100

treeModel.getMaxDepth

Int = 5

treeModel.getMaxMemoryInMB

Int = 256

treeModel.getMinInfoGain

Double = 0.01

treeModel.getMinInstancesPerNode

Int = 10

// 查看决策树

println("Learned classification tree model:\n" + treeModel.toDebugString)

Learned classification tree model:

DecisionTreeClassificationModel (uid=dtc_b950f91d35f8) of depth 5 with 43 nodes

// 例如“feature 7 in {0.0,1.0,2.0}”中的“{0.0,1.0,2.0}”

// 具体解释请参考本人博客http://www.cnblogs.com/wwxbi/p/6125493.html“VectorIndexer自动识别分类的特征，并对它们进行索引”

  If (feature 7 in {0.0,1.0,2.0})

   If (feature 7 in {0.0,2.0})

    If (feature 4 in {0.0,4.0})

     Predict: 1.0

    Else (feature 4 not in {0.0,4.0})

     If (feature 1 <= 32.0)

      If (feature 1 <= 27.0)

       Predict: 0.0

      Else (feature 1 > 27.0)

       Predict: 1.0

     Else (feature 1 > 32.0)

      If (feature 5 <= 16.0)

       Predict: 0.0

      Else (feature 5 > 16.0)

       Predict: 0.0

   Else (feature 7 not in {0.0,2.0})

    If (feature 4 in {0.0,1.0,3.0,4.0})

     If (feature 0 in {0.0})

      If (feature 2 <= 7.0)

       Predict: 0.0

      Else (feature 2 > 7.0)

       Predict: 0.0

     Else (feature 0 not in {0.0})

      Predict: 0.0

    Else (feature 4 not in {0.0,1.0,3.0,4.0})

     Predict: 1.0

  Else (feature 7 not in {0.0,1.0,2.0})

   If (feature 2 <= 4.0)

    If (feature 6 <= 3.0)

     If (feature 6 <= 1.0)

      Predict: 0.0

     Else (feature 6 > 1.0)

      Predict: 0.0

    Else (feature 6 > 3.0)

     If (feature 5 <= 16.0)

      If (feature 2 <= 0.75)

       Predict: 0.0

      Else (feature 2 > 0.75)

       Predict: 0.0

     Else (feature 5 > 16.0)

      If (feature 7 in {4.0})

       Predict: 0.0

      Else (feature 7 not in {4.0})

       Predict: 0.0

   Else (feature 2 > 4.0)

    If (feature 6 <= 3.0)

     If (feature 4 in {0.0,1.0,2.0})

      Predict: 0.0

     Else (feature 4 not in {0.0,1.0,2.0})

      If (feature 7 in {4.0})

       Predict: 0.0

      Else (feature 7 not in {4.0})

       Predict: 0.0

    Else (feature 6 > 3.0)

     If (feature 4 in {0.0,2.0,3.0,4.0})

      If (feature 6 <= 4.0)

       Predict: 0.0

      Else (feature 6 > 4.0)

       Predict: 0.0

     Else (feature 4 not in {0.0,2.0,3.0,4.0})

      If (feature 1 <= 37.0)

       Predict: 1.0

      Else (feature 1 > 37.0)

       Predict: 0.0

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