Packages

class DecisionTreeRegressionModel extends RegressionModel[Vector, DecisionTreeRegressionModel] with DecisionTreeModel with DecisionTreeRegressorParams with MLWritable with Serializable

Decision tree (Wikipedia) model for regression. It supports both continuous and categorical features.

Annotations
@Since( "1.4.0" )
Source
DecisionTreeRegressor.scala
Linear Supertypes
MLWritable, DecisionTreeRegressorParams, HasVarianceCol, TreeRegressorParams, HasVarianceImpurity, DecisionTreeParams, HasWeightCol, HasSeed, HasCheckpointInterval, DecisionTreeModel, RegressionModel[Vector, DecisionTreeRegressionModel], PredictionModel[Vector, DecisionTreeRegressionModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Model[DecisionTreeRegressionModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. DecisionTreeRegressionModel
  2. MLWritable
  3. DecisionTreeRegressorParams
  4. HasVarianceCol
  5. TreeRegressorParams
  6. HasVarianceImpurity
  7. DecisionTreeParams
  8. HasWeightCol
  9. HasSeed
  10. HasCheckpointInterval
  11. DecisionTreeModel
  12. RegressionModel
  13. PredictionModel
  14. PredictorParams
  15. HasPredictionCol
  16. HasFeaturesCol
  17. HasLabelCol
  18. Model
  19. Transformer
  20. PipelineStage
  21. Logging
  22. Params
  23. Serializable
  24. Serializable
  25. Identifiable
  26. AnyRef
  27. Any
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  1. Public
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Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. final val cacheNodeIds: BooleanParam

    If false, the algorithm will pass trees to executors to match instances with nodes.

    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)

    Definition Classes
    DecisionTreeParams
  7. final val checkpointInterval: IntParam

    Param for set checkpoint interval (>= 1) or disable checkpoint (-1).

    Param for set checkpoint interval (>= 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.

    Definition Classes
    HasCheckpointInterval
  8. final def clear(param: Param[_]): DecisionTreeRegressionModel.this.type

    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  9. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  10. def copy(extra: ParamMap): DecisionTreeRegressionModel

    Creates a copy of this instance with the same UID and some extra 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().

    Definition Classes
    DecisionTreeRegressionModelModelTransformerPipelineStageParams
    Annotations
    @Since( "1.4.0" )
  11. def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

    Copies param values from this instance to another instance for params shared by them.

    Copies param values from this instance to another instance for params shared by them.

    This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.

    to

    the target instance, which should work with the same set of default Params as this source instance

    extra

    extra params to be copied to the target's paramMap

    returns

    the target instance with param values copied

    Attributes
    protected
    Definition Classes
    Params
  12. final def defaultCopy[T <: Params](extra: ParamMap): T

    Default implementation of copy with extra params.

    Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.

    Attributes
    protected
    Definition Classes
    Params
  13. lazy val depth: Int

    Depth of the tree.

    Depth of the tree. E.g.: Depth 0 means 1 leaf node. Depth 1 means 1 internal node and 2 leaf nodes.

    Definition Classes
    DecisionTreeModel
  14. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  15. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  16. def explainParam(param: Param[_]): String

    Explains a param.

    Explains a param.

    param

    input param, must belong to this instance.

    returns

    a string that contains the input param name, doc, and optionally its default value and the user-supplied value

    Definition Classes
    Params
  17. def explainParams(): String

    Explains all params of this instance.

    Explains all params of this instance. See explainParam().

    Definition Classes
    Params
  18. def extractInstances(dataset: Dataset[_], validateInstance: (Instance) ⇒ Unit): RDD[Instance]

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types. Validate the output instances with the given function.

    Attributes
    protected
    Definition Classes
    PredictorParams
  19. def extractInstances(dataset: Dataset[_]): RDD[Instance]

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Attributes
    protected
    Definition Classes
    PredictorParams
  20. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  21. final def extractParamMap(extra: ParamMap): ParamMap

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Definition Classes
    Params
  22. lazy val featureImportances: Vector

    Estimate of the importance of each feature.

    Estimate of the importance of each feature.

    This generalizes the idea of "Gini" importance to other losses, following the explanation of Gini importance from "Random Forests" documentation by Leo Breiman and Adele Cutler, and following the implementation from scikit-learn.

    This feature importance is calculated as follows:

    • importance(feature j) = sum (over nodes which split on feature j) of the gain, where gain is scaled by the number of instances passing through node
    • Normalize importances for tree to sum to 1.
    Annotations
    @Since( "2.0.0" )
    Note

    Feature importance for single decision trees can have high variance due to correlated predictor variables. Consider using a RandomForestRegressor to determine feature importance instead.

  23. final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  24. def featuresDataType: DataType

    Returns the SQL DataType corresponding to the FeaturesType type parameter.

    Returns the SQL DataType corresponding to the FeaturesType type parameter.

    This is used by validateAndTransformSchema(). This workaround is needed since SQL has different APIs for Scala and Java.

    The default value is VectorUDT, but it may be overridden if FeaturesType is not Vector.

    Attributes
    protected
    Definition Classes
    PredictionModel
  25. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  26. final def get[T](param: Param[T]): Option[T]

    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

    Definition Classes
    Params
  27. final def getCacheNodeIds: Boolean

    Definition Classes
    DecisionTreeParams
  28. final def getCheckpointInterval: Int

    Definition Classes
    HasCheckpointInterval
  29. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  30. final def getDefault[T](param: Param[T]): Option[T]

    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes
    Params
  31. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  32. final def getImpurity: String

    Definition Classes
    HasVarianceImpurity
  33. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  34. final def getLeafCol: String

    Definition Classes
    DecisionTreeParams
    Annotations
    @Since( "3.0.0" )
  35. final def getMaxBins: Int

    Definition Classes
    DecisionTreeParams
  36. final def getMaxDepth: Int

    Definition Classes
    DecisionTreeParams
  37. final def getMaxMemoryInMB: Int

    Definition Classes
    DecisionTreeParams
  38. final def getMinInfoGain: Double

    Definition Classes
    DecisionTreeParams
  39. final def getMinInstancesPerNode: Int

    Definition Classes
    DecisionTreeParams
  40. final def getMinWeightFractionPerNode: Double

    Definition Classes
    DecisionTreeParams
  41. final def getOrDefault[T](param: Param[T]): T

    Gets the value of a param in the embedded param map or its default value.

    Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.

    Definition Classes
    Params
  42. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  43. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  44. final def getSeed: Long

    Definition Classes
    HasSeed
  45. final def getVarianceCol: String

    Definition Classes
    HasVarianceCol
  46. final def getWeightCol: String

    Definition Classes
    HasWeightCol
  47. final def hasDefault[T](param: Param[T]): Boolean

    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

    Definition Classes
    Params
  48. def hasParam(paramName: String): Boolean

    Tests whether this instance contains a param with a given name.

    Tests whether this instance contains a param with a given name.

    Definition Classes
    Params
  49. def hasParent: Boolean

    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes
    Model
  50. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  51. final val impurity: Param[String]

    Criterion used for information gain calculation (case-insensitive).

    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)

    Definition Classes
    HasVarianceImpurity
  52. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  53. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  54. final def isDefined(param: Param[_]): Boolean

    Checks whether a param is explicitly set or has a default value.

    Checks whether a param is explicitly set or has a default value.

    Definition Classes
    Params
  55. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  56. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  57. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  58. final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  59. final val leafCol: Param[String]

    Leaf indices column name.

    Leaf indices column name. Predicted leaf index of each instance in each tree by preorder. (default = "")

    Definition Classes
    DecisionTreeParams
    Annotations
    @Since( "3.0.0" )
  60. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  61. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  62. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  64. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  65. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  66. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  67. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  68. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  69. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  70. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  71. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  72. final val maxBins: IntParam

    Maximum number of bins used for discretizing continuous features and for choosing how to split on features at each node.

    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)

    Definition Classes
    DecisionTreeParams
  73. final val maxDepth: IntParam

    Maximum depth of the tree (nonnegative).

    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)

    Definition Classes
    DecisionTreeParams
  74. final val maxMemoryInMB: IntParam

    Maximum memory in MB allocated to histogram aggregation.

    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)

    Definition Classes
    DecisionTreeParams
  75. final val minInfoGain: DoubleParam

    Minimum information gain for a split to be considered at a tree node.

    Minimum information gain for a split to be considered at a tree node. Should be at least 0.0. (default = 0.0)

    Definition Classes
    DecisionTreeParams
  76. final val minInstancesPerNode: IntParam

    Minimum number of instances each child must have after split.

    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)

    Definition Classes
    DecisionTreeParams
  77. final val minWeightFractionPerNode: DoubleParam

    Minimum fraction of the weighted sample count that each child must have after split.

    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)

    Definition Classes
    DecisionTreeParams
  78. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  79. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  80. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  81. val numFeatures: Int

    Returns the number of features the model was trained on.

    Returns the number of features the model was trained on. If unknown, returns -1

    Definition Classes
    DecisionTreeRegressionModelPredictionModel
  82. def numNodes: Int

    Number of nodes in tree, including leaf nodes.

    Number of nodes in tree, including leaf nodes.

    Definition Classes
    DecisionTreeModel
  83. lazy val params: Array[Param[_]]

    Returns all params sorted by their names.

    Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.

    Definition Classes
    Params
    Note

    Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.

  84. var parent: Estimator[DecisionTreeRegressionModel]

    The parent estimator that produced this model.

    The parent estimator that produced this model.

    Definition Classes
    Model
    Note

    For ensembles' component Models, this value can be null.

  85. def predict(features: Vector): Double

    Predict label for the given features.

    Predict label for the given features. This method is used to implement transform() and output predictionCol.

    Definition Classes
    DecisionTreeRegressionModelPredictionModel
  86. def predictLeaf(features: Vector): Double

    returns

    The index of the leaf corresponding to the feature vector. Leaves are indexed in pre-order from 0.

    Definition Classes
    DecisionTreeModel
  87. def predictVariance(features: Vector): Double

    We need to update this function if we ever add other impurity measures.

    We need to update this function if we ever add other impurity measures.

    Attributes
    protected
  88. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  89. val rootNode: Node

    Root of the decision tree

    Root of the decision tree

    Definition Classes
    DecisionTreeRegressionModel → DecisionTreeModel
  90. def save(path: String): Unit

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  91. final val seed: LongParam

    Param for random seed.

    Param for random seed.

    Definition Classes
    HasSeed
  92. final def set(paramPair: ParamPair[_]): DecisionTreeRegressionModel.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  93. final def set(param: String, value: Any): DecisionTreeRegressionModel.this.type

    Sets a parameter (by name) in the embedded param map.

    Sets a parameter (by name) in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  94. final def set[T](param: Param[T], value: T): DecisionTreeRegressionModel.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  95. final def setDefault(paramPairs: ParamPair[_]*): DecisionTreeRegressionModel.this.type

    Sets default values for a list of params.

    Sets default values for a list of params.

    Note: Java developers should use the single-parameter setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.

    paramPairs

    a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.

    Attributes
    protected
    Definition Classes
    Params
  96. final def setDefault[T](param: Param[T], value: T): DecisionTreeRegressionModel.this.type

    Sets a default value for a param.

    Sets a default value for a param.

    param

    param to set the default value. Make sure that this param is initialized before this method gets called.

    value

    the default value

    Attributes
    protected
    Definition Classes
    Params
  97. def setFeaturesCol(value: String): DecisionTreeRegressionModel

    Definition Classes
    PredictionModel
  98. final def setLeafCol(value: String): DecisionTreeRegressionModel.this.type

    Definition Classes
    DecisionTreeParams
    Annotations
    @Since( "3.0.0" )
  99. def setParent(parent: Estimator[DecisionTreeRegressionModel]): DecisionTreeRegressionModel

    Sets the parent of this model (Java API).

    Sets the parent of this model (Java API).

    Definition Classes
    Model
  100. def setPredictionCol(value: String): DecisionTreeRegressionModel

    Definition Classes
    PredictionModel
  101. def setVarianceCol(value: String): DecisionTreeRegressionModel.this.type

  102. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  103. def toDebugString: String

    Full description of model

    Full description of model

    Definition Classes
    DecisionTreeModel
  104. def toString(): String

    Summary of the model

    Summary of the model

    Definition Classes
    DecisionTreeRegressionModel → DecisionTreeModel → Identifiable → AnyRef → Any
    Annotations
    @Since( "1.4.0" )
  105. def transform(dataset: Dataset[_]): DataFrame

    Transforms dataset by reading from featuresCol, calling predict, and storing the predictions as a new column predictionCol.

    Transforms dataset by reading from featuresCol, calling predict, and storing the predictions as a new column predictionCol.

    dataset

    input dataset

    returns

    transformed dataset with predictionCol of type Double

    Definition Classes
    DecisionTreeRegressionModelPredictionModelTransformer
    Annotations
    @Since( "2.0.0" )
  106. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

    Transforms the dataset with provided parameter map as additional parameters.

    Transforms the dataset with provided parameter map as additional parameters.

    dataset

    input dataset

    paramMap

    additional parameters, overwrite embedded params

    returns

    transformed dataset

    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  107. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

    Transforms the dataset with optional parameters

    Transforms the dataset with optional parameters

    dataset

    input dataset

    firstParamPair

    the first param pair, overwrite embedded params

    otherParamPairs

    other param pairs, overwrite embedded params

    returns

    transformed dataset

    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  108. def transformImpl(dataset: Dataset[_]): DataFrame
    Attributes
    protected
    Definition Classes
    PredictionModel
  109. def transformSchema(schema: StructType): StructType

    Check transform validity and derive the output schema from the input schema.

    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.

    Definition Classes
    DecisionTreeRegressionModelPredictionModelPipelineStage
    Annotations
    @Since( "1.4.0" )
  110. def transformSchema(schema: StructType, logging: Boolean): StructType

    :: DeveloperApi ::

    :: DeveloperApi ::

    Derives the output schema from the input schema and parameters, optionally with logging.

    This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.

    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  111. val uid: String

    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes
    DecisionTreeRegressionModelIdentifiable
  112. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

    Validates and transforms the input schema with the provided param map.

    Validates and transforms the input schema with the provided param map.

    schema

    input schema

    fitting

    whether this is in fitting

    featuresDataType

    SQL DataType for FeaturesType. E.g., VectorUDT for vector features.

    returns

    output schema

    Attributes
    protected
    Definition Classes
    DecisionTreeRegressorParams → PredictorParams
  113. final val varianceCol: Param[String]

    Param for Column name for the biased sample variance of prediction.

    Param for Column name for the biased sample variance of prediction.

    Definition Classes
    HasVarianceCol
  114. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  115. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  116. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  117. final val weightCol: Param[String]

    Param for weight column name.

    Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.

    Definition Classes
    HasWeightCol
  118. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    DecisionTreeRegressionModelMLWritable
    Annotations
    @Since( "2.0.0" )

Inherited from MLWritable

Inherited from DecisionTreeRegressorParams

Inherited from HasVarianceCol

Inherited from TreeRegressorParams

Inherited from HasVarianceImpurity

Inherited from DecisionTreeParams

Inherited from HasWeightCol

Inherited from HasSeed

Inherited from HasCheckpointInterval

Inherited from DecisionTreeModel

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Transformer

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

Members

Parameter setters

Parameter getters

(expert-only) Parameters

A list of advanced, expert-only (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

(expert-only) Parameter getters