Packages

class DecisionTreeClassifier extends ProbabilisticClassifier[Vector, DecisionTreeClassifier, DecisionTreeClassificationModel] with DecisionTreeClassifierParams with DefaultParamsWritable

Decision tree learning algorithm (http://en.wikipedia.org/wiki/Decision_tree_learning) for classification. It supports both binary and multiclass labels, as well as both continuous and categorical features.

Annotations
@Since( "1.4.0" )
Linear Supertypes
DefaultParamsWritable, MLWritable, DecisionTreeClassifierParams, TreeClassifierParams, DecisionTreeParams, HasSeed, HasCheckpointInterval, ProbabilisticClassifier[Vector, DecisionTreeClassifier, DecisionTreeClassificationModel], ProbabilisticClassifierParams, HasThresholds, HasProbabilityCol, Classifier[Vector, DecisionTreeClassifier, DecisionTreeClassificationModel], ClassifierParams, HasRawPredictionCol, Predictor[Vector, DecisionTreeClassifier, DecisionTreeClassificationModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Estimator[DecisionTreeClassificationModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. DecisionTreeClassifier
  2. DefaultParamsWritable
  3. MLWritable
  4. DecisionTreeClassifierParams
  5. TreeClassifierParams
  6. DecisionTreeParams
  7. HasSeed
  8. HasCheckpointInterval
  9. ProbabilisticClassifier
  10. ProbabilisticClassifierParams
  11. HasThresholds
  12. HasProbabilityCol
  13. Classifier
  14. ClassifierParams
  15. HasRawPredictionCol
  16. Predictor
  17. PredictorParams
  18. HasPredictionCol
  19. HasFeaturesCol
  20. HasLabelCol
  21. Estimator
  22. PipelineStage
  23. Logging
  24. Params
  25. Serializable
  26. Serializable
  27. Identifiable
  28. AnyRef
  29. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new DecisionTreeClassifier()
    Annotations
    @Since( "1.4.0" )
  2. new DecisionTreeClassifier(uid: String)
    Annotations
    @Since( "1.4.0" )

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[_]): DecisionTreeClassifier.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[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  10. def copy(extra: ParamMap): DecisionTreeClassifier

    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
    DecisionTreeClassifier → Predictor → Estimator → PipelineStage → Params
    Annotations
    @Since( "1.4.1" )
  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. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  14. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  15. 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
  16. def explainParams(): String

    Explains all params of this instance.

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

    Definition Classes
    Params
  17. def extractLabeledPoints(dataset: Dataset[_], numClasses: Int): RDD[LabeledPoint]

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    dataset

    DataFrame with columns for labels (org.apache.spark.sql.types.NumericType) and features (Vector).

    numClasses

    Number of classes label can take. Labels must be integers in the range [0, numClasses).

    Attributes
    protected
    Definition Classes
    Classifier
    Note

    Throws SparkException if any label is a non-integer or is negative

  18. def extractLabeledPoints(dataset: Dataset[_]): RDD[LabeledPoint]

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Attributes
    protected
    Definition Classes
    Predictor
  19. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  20. 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
  21. final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  22. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  23. def fit(dataset: Dataset[_]): DecisionTreeClassificationModel

    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes
    Predictor → Estimator
  24. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[DecisionTreeClassificationModel]

    Fits multiple models to the input data with multiple sets of parameters.

    Fits multiple models to the input data with multiple sets of parameters. The default implementation uses a for loop on each parameter map. Subclasses could override this to optimize multi-model training.

    dataset

    input dataset

    paramMaps

    An array of parameter maps. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted models, matching the input parameter maps

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  25. def fit(dataset: Dataset[_], paramMap: ParamMap): DecisionTreeClassificationModel

    Fits a single model to the input data with provided parameter map.

    Fits a single model to the input data with provided parameter map.

    dataset

    input dataset

    paramMap

    Parameter map. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  26. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DecisionTreeClassificationModel

    Fits a single model to the input data with optional parameters.

    Fits a single model to the input data with optional parameters.

    dataset

    input dataset

    firstParamPair

    the first param pair, overrides embedded params

    otherParamPairs

    other param pairs. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  27. 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
  28. final def getCacheNodeIds: Boolean

    Definition Classes
    DecisionTreeParams
  29. final def getCheckpointInterval: Int

    Definition Classes
    HasCheckpointInterval
  30. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  31. 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
  32. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  33. final def getImpurity: String

    Definition Classes
    TreeClassifierParams
  34. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  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. def getNumClasses(dataset: Dataset[_], maxNumClasses: Int = 100): Int

    Get the number of classes.

    Get the number of classes. This looks in column metadata first, and if that is missing, then this assumes classes are indexed 0,1,...,numClasses-1 and computes numClasses by finding the maximum label value.

    Label validation (ensuring all labels are integers >= 0) needs to be handled elsewhere, such as in extractLabeledPoints().

    dataset

    Dataset which contains a column labelCol

    maxNumClasses

    Maximum number of classes allowed when inferred from data. If numClasses is specified in the metadata, then maxNumClasses is ignored.

    returns

    number of classes

    Attributes
    protected
    Definition Classes
    Classifier
    Exceptions thrown

    IllegalArgumentException if metadata does not specify numClasses, and the actual numClasses exceeds maxNumClasses

  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 getProbabilityCol: String

    Definition Classes
    HasProbabilityCol
  45. final def getRawPredictionCol: String

    Definition Classes
    HasRawPredictionCol
  46. final def getSeed: Long

    Definition Classes
    HasSeed
  47. def getThresholds: Array[Double]

    Definition Classes
    HasThresholds
  48. 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
  49. 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
  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). Supported: "entropy" and "gini". (default = gini)

    Definition Classes
    TreeClassifierParams
  52. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): 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. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  60. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  61. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  62. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  64. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  65. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  66. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  67. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  68. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  69. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  70. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  71. 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 >= 2 and >= number of categories in any categorical feature. (default = 32)

    Definition Classes
    DecisionTreeParams
  72. final val maxDepth: IntParam

    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
  73. 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
  74. 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 >= 0.0. (default = 0.0)

    Definition Classes
    DecisionTreeParams
  75. 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. Should be >= 1. (default = 1)

    Definition Classes
    DecisionTreeParams
  76. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  77. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  78. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  79. 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.

  80. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  81. final val probabilityCol: Param[String]

    Param for Column name for predicted class conditional probabilities.

    Param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.

    Definition Classes
    HasProbabilityCol
  82. final val rawPredictionCol: Param[String]

    Param for raw prediction (a.k.a.

    Param for raw prediction (a.k.a. confidence) column name.

    Definition Classes
    HasRawPredictionCol
  83. 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( ... )
  84. final val seed: LongParam

    Param for random seed.

    Param for random seed.

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

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  86. final def set(param: String, value: Any): DecisionTreeClassifier.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
  87. final def set[T](param: Param[T], value: T): DecisionTreeClassifier.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  88. def setCacheNodeIds(value: Boolean): DecisionTreeClassifier.this.type

    Annotations
    @Since( "1.4.0" )
  89. def setCheckpointInterval(value: Int): DecisionTreeClassifier.this.type

    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 at least 1. (default = 10)

    Annotations
    @Since( "1.4.0" )
  90. final def setDefault(paramPairs: ParamPair[_]*): DecisionTreeClassifier.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
  91. final def setDefault[T](param: Param[T], value: T): DecisionTreeClassifier.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
  92. def setFeaturesCol(value: String): DecisionTreeClassifier

    Definition Classes
    Predictor
  93. def setImpurity(value: String): DecisionTreeClassifier.this.type

    Annotations
    @Since( "1.4.0" )
  94. def setLabelCol(value: String): DecisionTreeClassifier

    Definition Classes
    Predictor
  95. def setMaxBins(value: Int): DecisionTreeClassifier.this.type

    Annotations
    @Since( "1.4.0" )
  96. def setMaxDepth(value: Int): DecisionTreeClassifier.this.type

    Annotations
    @Since( "1.4.0" )
  97. def setMaxMemoryInMB(value: Int): DecisionTreeClassifier.this.type

    Annotations
    @Since( "1.4.0" )
  98. def setMinInfoGain(value: Double): DecisionTreeClassifier.this.type

    Annotations
    @Since( "1.4.0" )
  99. def setMinInstancesPerNode(value: Int): DecisionTreeClassifier.this.type

    Annotations
    @Since( "1.4.0" )
  100. def setPredictionCol(value: String): DecisionTreeClassifier

    Definition Classes
    Predictor
  101. def setProbabilityCol(value: String): DecisionTreeClassifier

    Definition Classes
    ProbabilisticClassifier
  102. def setRawPredictionCol(value: String): DecisionTreeClassifier

    Definition Classes
    Classifier
  103. def setSeed(value: Long): DecisionTreeClassifier.this.type

    Annotations
    @Since( "1.6.0" )
  104. def setThresholds(value: Array[Double]): DecisionTreeClassifier

    Definition Classes
    ProbabilisticClassifier
  105. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  106. final val thresholds: DoubleArrayParam

    Param for Thresholds in multi-class classification to adjust the probability of predicting each class.

    Param for Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.

    Definition Classes
    HasThresholds
  107. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  108. def train(dataset: Dataset[_]): DecisionTreeClassificationModel

    Train a model using the given dataset and parameters.

    Train a model using the given dataset and parameters. Developers can implement this instead of fit() to avoid dealing with schema validation and copying parameters into the model.

    dataset

    Training dataset

    returns

    Fitted model

    Attributes
    protected
    Definition Classes
    DecisionTreeClassifier → Predictor
  109. def transformSchema(schema: StructType): StructType

    :: DeveloperApi ::

    :: DeveloperApi ::

    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
    Predictor → PipelineStage
  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
    DecisionTreeClassifier → Identifiable
    Annotations
    @Since( "1.4.0" )
  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
    ProbabilisticClassifierParams → ClassifierParams → PredictorParams
  113. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  114. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  115. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  116. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    DefaultParamsWritable → MLWritable

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from DecisionTreeClassifierParams

Inherited from TreeClassifierParams

Inherited from DecisionTreeParams

Inherited from HasSeed

Inherited from HasCheckpointInterval

Inherited from ProbabilisticClassifier[Vector, DecisionTreeClassifier, DecisionTreeClassificationModel]

Inherited from ProbabilisticClassifierParams

Inherited from HasThresholds

Inherited from HasProbabilityCol

Inherited from Classifier[Vector, DecisionTreeClassifier, DecisionTreeClassificationModel]

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from Predictor[Vector, DecisionTreeClassifier, DecisionTreeClassificationModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Estimator[DecisionTreeClassificationModel]

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