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

Spark2 ML包之决策树分类Decision tree classifier详细解说的更多相关文章

【分类算法】决策树（Decision Tree）
(注:本篇博文是对<统计学习方法>中决策树一章的归纳总结,下列的一些文字和图例均引自此书~) 决策树(decision tree)属于分类/回归方法.其具有可读性.可解释性.分类速度快等优 ...
2. 决策树（Decision Tree）-ID3、C4.5、CART比较
1. 决策树(Decision Tree)-决策树原理 2. 决策树(Decision Tree)-ID3.C4.5.CART比较 1. 前言上文决策树(Decision Tree)1-决策树原理介 ...
1. 决策树（Decision Tree）-决策树原理
1. 决策树(Decision Tree)-决策树原理 2. 决策树(Decision Tree)-ID3.C4.5.CART比较 1. 前言决策树是一种基本的分类和回归方法.决策树呈树形结构,在分 ...
【机器学习实战】第3章决策树（Decision Tree）
第3章决策树 <script type="text/javascript" src="http://cdn.mathjax.org/mathjax/latest/ ...
【机器学习】决策树（Decision Tree）学习笔记
[机器学习]决策树(decision tree) 学习笔记标签(空格分隔): 机器学习决策树简介决策树(decision tree)是一个树结构(可以是二叉树或非二叉树).其每个非叶节点表示一个 ...
决策树（decision tree）
决策树是一种常见的机器学习模型.形象地说,决策树对应着我们直观上做决策的过程:经由一系列判断,得到最终决策.由此,我们引出决策树模型. 一.决策树的基本流程决策树的跟节点包含全部样例,叶节点则对应决 ...
Python机器学习算法 — 决策树（Decision Tree）
决策树 -- 简介决策树(decision tree)一般都是自上而下的来生成的.每个决策或事件(即自然状态)都可能引出两个或多个事件,导致不同的结果,把这种决策分支画成图形很像一棵 ...
决策树（Decision Tree
转化自:https://trainings.analyticsvidhya.com/courses/course-v1:AnalyticsVidhya+LPDS2019+LPDS2019_T1/cou ...
决策树（decision tree）
内容学习于 ApacheCN github 定义: 分类决策树模型是一种描述对实例进行分类的树形结构.决策树由结点(node)和有向边(directed edge)组成.结点有两种类型:内部结点(in ...

随机推荐

Unity三消算法
消除算法图文详解三消算法首要实现的就是找到所有三个或三个以上的可消除对象,但直接找到这些对象是不太现实的,所以我们要将需求拆分.可不可以先获取所有图案相连的对象,进而在获取三消对象,这个算法也是众多 ...
QT基础：QT 定时器学习
定时器在编程中经常要用到,有必要学习一下,记记笔记! Qt中定时器的使用有两种方法,一种是使用QObject类提供的定时器,还有一种就是使用QTimer类. 1.QObject中的定时器的使用,需要用 ...
【Python】paddlepaddle docker python2.7 centos运行
PS E:\AI\BlockMining> docker cp .\LearnPaddle-master\ 30acd98dbc60:\rootPS E:\AI\BlockMining> ...
PHP代码审计笔记--CSRF漏洞
0x01 前言 CSRF(Cross-site request forgery)跨站请求伪造.攻击者盗用了你的身份,以你的名义向第三方网站发送恶意请求,对服务器来说这个请求是完全合法的,但是却完成了攻 ...
asp.net 验证码
Before proceeding with the topic first we must understand "What is a Captcha code?" and &q ...
使用 urllib 发送请求
urllib.request.urlopen(url, data=None, timeout=n) 用于发送HTTP请求并得到响应内容 In []: import urllib.request In ...
带有ZLIB_LIBRARY_DEBUG的FindZLIB.cmake文件
CMake自带的FindZLIB.cmake只有ZLIB_LIBRARY,而没有ZLIB_LIBRARY_DEBUG 将下面的代码保存成FindZLIB.cmake,替换掉D:\Program Fil ...
LVS+NGINX+TOMCAT_集群实施操作记录.docx
LVS IP: Eth0:192.168.100.115 Eth1:192.168.100.215 Vi /etc/init.d./lvs #!/bin/sh # # lvs Start ...
JQuery插件的使用
今天就来总结一下jquery插件的使用,刚开始学习时间一直自己在写实现他的功能,现在发现jquery好强大啊,好多的功能都给我们封装好啦,jquery万岁,嘿嘿,下面就来简单的总结一下我们学习的:
java web当中表单提交到后台出现乱码的解决方法
1.如果提交方式为post,想不乱码,只需要在服务器端设置request对象的编码即可,客户端以哪种编码提交的,服务器端的request对象就以对应的编码接收,比如客户端是以UTF-8编码提交的,那么 ...

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

Spark2 ML包之决策树分类Decision tree classifier详细解说的更多相关文章

随机推荐

热门专题