Spark2 ML包之决策树分类Decision tree classifier详细解说
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
