class RegressionTree extends Model[RegressionTreeResult]
Container holding a model node, encoders, and the feature influences
- Alphabetic
- By Inheritance
- RegressionTree
- Model
- Serializable
- AnyRef
- Any
- Hide All
- Show All
- Public
- Protected
Instance Constructors
- new RegressionTree(root: ModelNode[PredictionResult[Double]], encoders: Seq[Option[CategoricalEncoder[Any]]])
- root
of the tree
- encoders
for categorical variables
Value Members
- final def !=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- final def ##: Int
- Definition Classes
- AnyRef → Any
- final def ==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- final def asInstanceOf[T0]: T0
- Definition Classes
- Any
- def clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.CloneNotSupportedException]) @native()
- final def eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- def equals(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef → Any
- def finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.Throwable])
- final def getClass(): Class[_ <: AnyRef]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
- def hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
- final def isInstanceOf[T0]: Boolean
- Definition Classes
- Any
- final def ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- final def notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
- final def notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
- def shapley(input: Vector[Any], omitFeatures: Set[Int] = Set()): Option[DenseMatrix[Double]]
Compute Shapley feature attributions for a given input
Compute Shapley feature attributions for a given input
- input
for which to compute feature attributions.
- omitFeatures
feature indices to omit in computing Shapley values
- returns
array of Shapley feature attributions, one per input feature, each a vector of One Vector[Double] per feature, each of length equal to the output dimension. The output dimension is 1 for single-task regression, or equal to the number of classification categories.
- Definition Classes
- RegressionTree → Model
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
- AnyRef
- def toString(): String
- Definition Classes
- AnyRef → Any
- def transform(inputs: Seq[Vector[Any]]): RegressionTreeResult
Apply the model by calling predict and wrapping the results
Apply the model by calling predict and wrapping the results
- inputs
to apply the model to
- returns
a prediction result which includes only the expected outputs
- Definition Classes
- RegressionTree → Model
- final def wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException]) @native()