Package org.apache.spark.ml.regression

Class DecisionTreeRegressionModel

Object
org.apache.spark.ml.PipelineStage
org.apache.spark.ml.Transformer
org.apache.spark.ml.Model<M>
org.apache.spark.ml.PredictionModel<FeaturesType,M>
org.apache.spark.ml.regression.RegressionModel<Vector,DecisionTreeRegressionModel>
org.apache.spark.ml.regression.DecisionTreeRegressionModel
All Implemented Interfaces:
Serializable, org.apache.spark.internal.Logging, Params, HasCheckpointInterval, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasSeed, HasVarianceCol, HasWeightCol, PredictorParams, DecisionTreeModel, DecisionTreeParams, DecisionTreeRegressorParams, HasVarianceImpurity, TreeRegressorParams, Identifiable, MLWritable, scala.Serializable
public class DecisionTreeRegressionModel extends RegressionModel<Vector,DecisionTreeRegressionModel> implements DecisionTreeModel, DecisionTreeRegressorParams, MLWritable, scala.Serializable
Decision tree (Wikipedia) model for regression. It supports both continuous and categorical features.

param: rootNode Root of the decision tree

See Also:

  • Method Details

    • read

      public static MLReader<DecisionTreeRegressionModel> read()

    • load

      public static DecisionTreeRegressionModel load(String path)

    • varianceCol

      public final Param<String> varianceCol()
      Description copied from interface: HasVarianceCol
      Param for Column name for the biased sample variance of prediction.
      Specified by:
      varianceCol in interface HasVarianceCol
      Returns:
      (undocumented)

    • impurity

      public final Param<String> impurity()
      Description copied from interface: HasVarianceImpurity
      Criterion used for information gain calculation (case-insensitive). This impurity type is used in DecisionTreeRegressor, RandomForestRegressor, GBTRegressor and GBTClassifier (since GBTClassificationModel is internally composed of DecisionTreeRegressionModels). Supported: "variance". (default = variance)
      Specified by:
      impurity in interface HasVarianceImpurity
      Returns:
      (undocumented)

    • leafCol

      public final Param<String> leafCol()
      Description copied from interface: DecisionTreeParams
      Leaf indices column name. Predicted leaf index of each instance in each tree by preorder. (default = "")
      Specified by:
      leafCol in interface DecisionTreeParams
      Returns:
      (undocumented)

    • maxDepth

      public final IntParam maxDepth()
      Description copied from interface: DecisionTreeParams
      Maximum depth of the tree (nonnegative). E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. (default = 5)
      Specified by:
      maxDepth in interface DecisionTreeParams
      Returns:
      (undocumented)

    • maxBins

      public final IntParam maxBins()
      Description copied from interface: DecisionTreeParams
      Maximum number of bins used for discretizing continuous features and for choosing how to split on features at each node. More bins give higher granularity. Must be at least 2 and at least number of categories in any categorical feature. (default = 32)
      Specified by:
      maxBins in interface DecisionTreeParams
      Returns:
      (undocumented)

    • minInstancesPerNode

      public final IntParam minInstancesPerNode()
      Description copied from interface: DecisionTreeParams
      Minimum number of instances each child must have after split. If a split causes the left or right child to have fewer than minInstancesPerNode, the split will be discarded as invalid. Must be at least 1. (default = 1)
      Specified by:
      minInstancesPerNode in interface DecisionTreeParams
      Returns:
      (undocumented)

    • minWeightFractionPerNode

      public final DoubleParam minWeightFractionPerNode()
      Description copied from interface: DecisionTreeParams
      Minimum fraction of the weighted sample count that each child must have after split. If a split causes the fraction of the total weight in the left or right child to be less than minWeightFractionPerNode, the split will be discarded as invalid. Should be in the interval [0.0, 0.5). (default = 0.0)
      Specified by:
      minWeightFractionPerNode in interface DecisionTreeParams
      Returns:
      (undocumented)

    • minInfoGain

      public final DoubleParam minInfoGain()
      Description copied from interface: DecisionTreeParams
      Minimum information gain for a split to be considered at a tree node. Should be at least 0.0. (default = 0.0)
      Specified by:
      minInfoGain in interface DecisionTreeParams
      Returns:
      (undocumented)

    • maxMemoryInMB

      public final IntParam maxMemoryInMB()
      Description copied from interface: DecisionTreeParams
      Maximum memory in MB allocated to histogram aggregation. If too small, then 1 node will be split per iteration, and its aggregates may exceed this size. (default = 256 MB)
      Specified by:
      maxMemoryInMB in interface DecisionTreeParams
      Returns:
      (undocumented)

    • cacheNodeIds

      public final BooleanParam cacheNodeIds()
      Description copied from interface: DecisionTreeParams
      If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees. Users can set how often should the cache be checkpointed or disable it by setting checkpointInterval. (default = false)
      Specified by:
      cacheNodeIds in interface DecisionTreeParams
      Returns:
      (undocumented)

    • weightCol

      public final Param<String> weightCol()
      Description copied from interface: HasWeightCol
      Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.
      Specified by:
      weightCol in interface HasWeightCol
      Returns:
      (undocumented)

    • seed

      public final LongParam seed()
      Description copied from interface: HasSeed
      Param for random seed.
      Specified by:
      seed in interface HasSeed
      Returns:
      (undocumented)

    • checkpointInterval

      public final IntParam checkpointInterval()
      Description copied from interface: HasCheckpointInterval
      Param for set checkpoint interval (&gt;= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations. Note: this setting will be ignored if the checkpoint directory is not set in the SparkContext.
      Specified by:
      checkpointInterval in interface HasCheckpointInterval
      Returns:
      (undocumented)

    • depth

      public int depth()
      Description copied from interface: DecisionTreeModel
      Depth of the tree. E.g.: Depth 0 means 1 leaf node. Depth 1 means 1 internal node and 2 leaf nodes.
      Specified by:
      depth in interface DecisionTreeModel
      Returns:
      (undocumented)

    • uid

      public String uid()
      Description copied from interface: Identifiable
      An immutable unique ID for the object and its derivatives.
      Specified by:
      uid in interface Identifiable
      Returns:
      (undocumented)

    • rootNode

      public Node rootNode()
      Description copied from interface: DecisionTreeModel
      Root of the decision tree
      Specified by:
      rootNode in interface DecisionTreeModel

    • numFeatures

      public int numFeatures()
      Description copied from class: PredictionModel
      Returns the number of features the model was trained on. If unknown, returns -1
      Overrides:
      numFeatures in class PredictionModel<Vector,DecisionTreeRegressionModel>

    • setVarianceCol

      public DecisionTreeRegressionModel setVarianceCol(String value)

    • predict

      public double predict(Vector features)
      Description copied from class: PredictionModel
      Predict label for the given features. This method is used to implement transform() and output PredictionModel.predictionCol().
      Specified by:
      predict in class PredictionModel<Vector,DecisionTreeRegressionModel>
      Parameters:
      features - (undocumented)
      Returns:
      (undocumented)

    • transformSchema

      public StructType transformSchema(StructType schema)
      Description copied from class: PipelineStage
      Check transform validity and derive the output schema from the input schema.

      We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

      Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

      Overrides:
      transformSchema in class PredictionModel<Vector,DecisionTreeRegressionModel>
      Parameters:
      schema - (undocumented)
      Returns:
      (undocumented)

    • transform

      public Dataset<Row> transform(Dataset<?> dataset)
      Description copied from class: PredictionModel
      Transforms dataset by reading from PredictionModel.featuresCol(), calling predict, and storing the predictions as a new column PredictionModel.predictionCol().

      Overrides:
      transform in class PredictionModel<Vector,DecisionTreeRegressionModel>
      Parameters:
      dataset - input dataset
      Returns:
      transformed dataset with PredictionModel.predictionCol() of type Double

    • copy

      public DecisionTreeRegressionModel copy(ParamMap extra)
      Description copied from interface: Params
      Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().
      Specified by:
      copy in interface Params
      Specified by:
      copy in class Model<DecisionTreeRegressionModel>
      Parameters:
      extra - (undocumented)
      Returns:
      (undocumented)

    • toString

      public String toString()
      Description copied from interface: DecisionTreeModel
      Summary of the model
      Specified by:
      toString in interface DecisionTreeModel
      Specified by:
      toString in interface Identifiable
      Overrides:
      toString in class Object

    • featureImportances

      public Vector featureImportances()

    • write

      public MLWriter write()
      Description copied from interface: MLWritable
      Returns an MLWriter instance for this ML instance.
      Specified by:
      write in interface MLWritable
      Returns:
      (undocumented)