Class/Object

org.apache.spark.ml.regression

RandomForestRegressionModel

Related Docs: object RandomForestRegressionModel | package regression

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class RandomForestRegressionModel extends PredictionModel[Vector, RandomForestRegressionModel] with RandomForestRegressionModelParams with TreeEnsembleModel[DecisionTreeRegressionModel] with MLWritable with Serializable

Random Forest model for regression. It supports both continuous and categorical features.

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

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

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    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  5. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  6. final val cacheNodeIds: BooleanParam

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    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

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    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.

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

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    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

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  10. def copy(extra: ParamMap): RandomForestRegressionModel

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

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    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

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    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. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  14. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  15. def explainParam(param: Param[_]): String

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    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
  16. def explainParams(): String

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    Explains all params of this instance.

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

    Definition Classes
    Params
  17. final def extractParamMap(): ParamMap

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    extractParamMap with no extra values.

    extractParamMap with no extra values.

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

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    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
  19. lazy val featureImportances: Vector

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    Estimate of the importance of each feature.

    Estimate of the importance of each feature.

    Each feature's importance is the average of its importance across all trees in the ensemble The importance vector is normalized to sum to 1. This method is suggested by Hastie et al. (Hastie, Tibshirani, Friedman. "The Elements of Statistical Learning, 2nd Edition." 2001.) and follows the implementation from scikit-learn.

    Annotations
    @Since( "1.5.0" )
    See also

    DecisionTreeRegressionModel.featureImportances

  20. final val featureSubsetStrategy: Param[String]

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    The number of features to consider for splits at each tree node.

    The number of features to consider for splits at each tree node. Supported options:

    • "auto": Choose automatically for task: If numTrees == 1, set to "all." If numTrees > 1 (forest), set to "sqrt" for classification and to "onethird" for regression.
    • "all": use all features
    • "onethird": use 1/3 of the features
    • "sqrt": use sqrt(number of features)
    • "log2": use log2(number of features)
    • "n": when n is in the range (0, 1.0], use n * number of features. When n is in the range (1, number of features), use n features. (default = "auto")

    These various settings are based on the following references:

    • log2: tested in Breiman (2001)
    • sqrt: recommended by Breiman manual for random forests
    • The defaults of sqrt (classification) and onethird (regression) match the R randomForest package.
    Definition Classes
    HasFeatureSubsetStrategy
    See also

    Breiman manual for random forests

    Breiman (2001)

  21. final val featuresCol: Param[String]

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    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  22. def featuresDataType: DataType

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    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
  23. def finalize(): Unit

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    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  24. final def get[T](param: Param[T]): Option[T]

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    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

    Definition Classes
    Params
  25. final def getCacheNodeIds: Boolean

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    Definition Classes
    DecisionTreeParams
  26. final def getCheckpointInterval: Int

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    Definition Classes
    HasCheckpointInterval
  27. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  28. final def getDefault[T](param: Param[T]): Option[T]

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    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes
    Params
  29. final def getFeatureSubsetStrategy: String

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    Definition Classes
    HasFeatureSubsetStrategy
  30. final def getFeaturesCol: String

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    Definition Classes
    HasFeaturesCol
  31. final def getImpurity: String

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    Definition Classes
    TreeRegressorParams
  32. final def getLabelCol: String

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    Definition Classes
    HasLabelCol
  33. final def getMaxBins: Int

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    Definition Classes
    DecisionTreeParams
  34. final def getMaxDepth: Int

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    Definition Classes
    DecisionTreeParams
  35. final def getMaxMemoryInMB: Int

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    Definition Classes
    DecisionTreeParams
  36. final def getMinInfoGain: Double

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    Definition Classes
    DecisionTreeParams
  37. final def getMinInstancesPerNode: Int

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    Definition Classes
    DecisionTreeParams
  38. val getNumTrees: Int

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    Number of trees in ensemble

    Number of trees in ensemble

    Definition Classes
    TreeEnsembleModel
  39. final def getOrDefault[T](param: Param[T]): T

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    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
  40. def getParam(paramName: String): Param[Any]

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    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  41. final def getPredictionCol: String

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    Definition Classes
    HasPredictionCol
  42. final def getSeed: Long

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    Definition Classes
    HasSeed
  43. final def getSubsamplingRate: Double

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    Definition Classes
    TreeEnsembleParams
  44. final def hasDefault[T](param: Param[T]): Boolean

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    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

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

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    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
  46. def hasParent: Boolean

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    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes
    Model
  47. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  48. final val impurity: Param[String]

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

    Criterion used for information gain calculation (case-insensitive). Supported: "variance". (default = variance)

    Definition Classes
    TreeRegressorParams
  49. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  50. final def isDefined(param: Param[_]): Boolean

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    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
  51. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  52. final def isSet(param: Param[_]): Boolean

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    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  53. def isTraceEnabled(): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  54. final val labelCol: Param[String]

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    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  55. def log: Logger

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    Attributes
    protected
    Definition Classes
    Logging
  56. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  57. def logDebug(msg: ⇒ String): Unit

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    protected
    Definition Classes
    Logging
  58. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    protected
    Definition Classes
    Logging
  59. def logError(msg: ⇒ String): Unit

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    Definition Classes
    Logging
  60. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    Definition Classes
    Logging
  61. def logInfo(msg: ⇒ String): Unit

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    Definition Classes
    Logging
  62. def logName: String

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    Definition Classes
    Logging
  63. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    Definition Classes
    Logging
  64. def logTrace(msg: ⇒ String): Unit

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    protected
    Definition Classes
    Logging
  65. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    Definition Classes
    Logging
  66. def logWarning(msg: ⇒ String): Unit

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    protected
    Definition Classes
    Logging
  67. final val maxBins: IntParam

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    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 >= 2 and >= number of categories in any categorical feature. (default = 32)

    Definition Classes
    DecisionTreeParams
  68. final val maxDepth: IntParam

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    Maximum depth of the tree (>= 0).

    Maximum depth of the tree (>= 0). E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. (default = 5)

    Definition Classes
    DecisionTreeParams
  69. final val maxMemoryInMB: IntParam

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    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
  70. final val minInfoGain: DoubleParam

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    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. (default = 0.0)

    Definition Classes
    DecisionTreeParams
  71. final val minInstancesPerNode: IntParam

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    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. Should be >= 1. (default = 1)

    Definition Classes
    DecisionTreeParams
  72. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  73. final def notify(): Unit

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    Definition Classes
    AnyRef
  74. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  75. val numFeatures: Int

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    Number of features used by this model

    Number of features used by this model

    Definition Classes
    RandomForestRegressionModelPredictionModel
  76. lazy val params: Array[Param[_]]

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    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.

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

    Definition Classes
    Params
  77. var parent: Estimator[RandomForestRegressionModel]

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    The parent estimator that produced this model.

    The parent estimator that produced this model. Note: For ensembles' component Models, this value can be null.

    Definition Classes
    Model
  78. def predict(features: Vector): Double

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    Predict label for the given features.

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

    Attributes
    protected
    Definition Classes
    RandomForestRegressionModelPredictionModel
  79. final val predictionCol: Param[String]

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    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  80. def save(path: String): Unit

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    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( ... )
  81. final val seed: LongParam

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    Param for random seed.

    Param for random seed.

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

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    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

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

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    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
  84. final def set[T](param: Param[T], value: T): RandomForestRegressionModel.this.type

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    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  85. def setCacheNodeIds(value: Boolean): RandomForestRegressionModel.this.type

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    Definition Classes
    DecisionTreeParams
  86. def setCheckpointInterval(value: Int): RandomForestRegressionModel.this.type

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    Specifies how often to checkpoint the cached node IDs.

    Specifies how often to checkpoint the cached node IDs. E.g. 10 means that the cache will get checkpointed every 10 iterations. This is only used if cacheNodeIds is true and if the checkpoint directory is set in org.apache.spark.SparkContext. Must be >= 1. (default = 10)

    Definition Classes
    DecisionTreeParams
  87. final def setDefault(paramPairs: ParamPair[_]*): RandomForestRegressionModel.this.type

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    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
  88. final def setDefault[T](param: Param[T], value: T): RandomForestRegressionModel.this.type

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    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
  89. def setFeatureSubsetStrategy(value: String): RandomForestRegressionModel.this.type

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    Definition Classes
    HasFeatureSubsetStrategy
  90. def setFeaturesCol(value: String): RandomForestRegressionModel

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    Definition Classes
    PredictionModel
  91. def setImpurity(value: String): RandomForestRegressionModel.this.type

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    Definition Classes
    TreeRegressorParams
  92. def setMaxBins(value: Int): RandomForestRegressionModel.this.type

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    Definition Classes
    DecisionTreeParams
  93. def setMaxDepth(value: Int): RandomForestRegressionModel.this.type

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    Definition Classes
    DecisionTreeParams
  94. def setMaxMemoryInMB(value: Int): RandomForestRegressionModel.this.type

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    Definition Classes
    DecisionTreeParams
  95. def setMinInfoGain(value: Double): RandomForestRegressionModel.this.type

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    Definition Classes
    DecisionTreeParams
  96. def setMinInstancesPerNode(value: Int): RandomForestRegressionModel.this.type

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    Definition Classes
    DecisionTreeParams
  97. def setParent(parent: Estimator[RandomForestRegressionModel]): RandomForestRegressionModel

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    Sets the parent of this model (Java API).

    Sets the parent of this model (Java API).

    Definition Classes
    Model
  98. def setPredictionCol(value: String): RandomForestRegressionModel

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    Definition Classes
    PredictionModel
  99. def setSeed(value: Long): RandomForestRegressionModel.this.type

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    Definition Classes
    DecisionTreeParams
  100. def setSubsamplingRate(value: Double): RandomForestRegressionModel.this.type

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    Definition Classes
    TreeEnsembleParams
  101. final val subsamplingRate: DoubleParam

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    Fraction of the training data used for learning each decision tree, in range (0, 1].

    Fraction of the training data used for learning each decision tree, in range (0, 1]. (default = 1.0)

    Definition Classes
    TreeEnsembleParams
  102. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  103. def toDebugString: String

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    Full description of model

    Full description of model

    Definition Classes
    TreeEnsembleModel
  104. def toString(): String

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    Summary of the model

    Summary of the model

    Definition Classes
    RandomForestRegressionModel → TreeEnsembleModel → Identifiable → AnyRef → Any
    Annotations
    @Since( "1.4.0" )
  105. lazy val totalNumNodes: Int

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    Total number of nodes, summed over all trees in the ensemble.

    Total number of nodes, summed over all trees in the ensemble.

    Definition Classes
    TreeEnsembleModel
  106. def transform(dataset: Dataset[_]): DataFrame

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    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
    PredictionModelTransformer
  107. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

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    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" )
  108. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

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    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()
  109. def transformImpl(dataset: Dataset[_]): DataFrame

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    Attributes
    protected
    Definition Classes
    RandomForestRegressionModelPredictionModel
  110. def transformSchema(schema: StructType): StructType

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    :: DeveloperApi ::

    :: DeveloperApi ::

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

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

    Definition Classes
    PredictionModelPipelineStage
  111. def transformSchema(schema: StructType, logging: Boolean): StructType

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    :: 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()
  112. def treeWeights: Array[Double]

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    Weights for each tree, zippable with trees

    Weights for each tree, zippable with trees

    Definition Classes
    RandomForestRegressionModel → TreeEnsembleModel
    Annotations
    @Since( "1.4.0" )
  113. def trees: Array[DecisionTreeRegressionModel]

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    Trees in this ensemble.

    Trees in this ensemble. Warning: These have null parent Estimators.

    Definition Classes
    RandomForestRegressionModel → TreeEnsembleModel
    Annotations
    @Since( "1.4.0" )
  114. val uid: String

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    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes
    RandomForestRegressionModelIdentifiable
  115. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

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    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., org.apache.spark.mllib.linalg.VectorUDT for vector features.

    returns

    output schema

    Attributes
    protected
    Definition Classes
    PredictorParams
  116. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  117. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  118. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  119. def write: MLWriter

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    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    RandomForestRegressionModelMLWritable
    Annotations
    @Since( "2.0.0" )

Deprecated Value Members

  1. val numTrees: Int

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    Number of trees in ensemble

    Number of trees in ensemble

    Annotations
    @deprecated
    Deprecated

    (Since version 2.0.0) Use getNumTrees instead. This method will be removed in 2.1.0.

  2. def validateParams(): Unit

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    Validates parameter values stored internally.

    Validates parameter values stored internally. Raise an exception if any parameter value is invalid.

    This only needs to check for interactions between parameters. Parameter value checks which do not depend on other parameters are handled by Param.validate(). This method does not handle input/output column parameters; those are checked during schema validation.

    Definition Classes
    Params
    Annotations
    @deprecated
    Deprecated

    (Since version 2.0.0) Will be removed in 2.1.0. Checks should be merged into transformSchema.

Inherited from MLWritable

Inherited from TreeEnsembleModel[DecisionTreeRegressionModel]

Inherited from RandomForestRegressionModelParams

Inherited from TreeRegressorParams

Inherited from HasFeatureSubsetStrategy

Inherited from TreeEnsembleParams

Inherited from DecisionTreeParams

Inherited from HasSeed

Inherited from HasCheckpointInterval

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 setters

(expert-only) Parameter getters