package extratrees
Linear Supertypes
Ordering
- Alphabetic
- By Inheritance
Inherited
- extratrees
- AnyRef
- Any
- Hide All
- Show All
Visibility
- Public
- Protected
Type Members
- case class ClassificationLeaf(targetDistribution: Seq[Double]) extends ClassificationTree with Product with Serializable
- case class ClassificationNonLeaf(left: ClassificationTree, right: ClassificationTree, splitFeature: Int, cutpoint: Double, splitMissingIsLess: Boolean) extends ClassificationTree with Product with Serializable
- sealed trait ClassificationTree extends AnyRef
- case class RegressionLeaf(targetMean: Double) extends RegressionTree with Product with Serializable
- case class RegressionNonLeaf(left: RegressionTree, right: RegressionTree, splitFeature: Int, cutpoint: Double, splitMissingIsLess: Boolean) extends RegressionTree with Product with Serializable
- sealed trait RegressionTree extends AnyRef
Value Members
- def buildForestClassification(data: Mat[Double], target: Vec[Int], sampleWeights: Option[Vec[Double]], numClasses: Int, nMin: Int, k: Int, m: Int, parallelism: Int, seed: Long = java.time.Instant.now.toEpochMilli): Seq[ClassificationTree]
Train an extratrees classifier forest
Train an extratrees classifier forest
- nMin
minimum sample size for splitting a node
- k
number of features to consider in each split step. The best among these will be chosen.
- m
number of trees
- seed
Returns a list of ClassificationTree objects which can be passed to
predictClassification
- def buildForestRegression(data: Mat[Double], target: Vec[Double], nMin: Int, k: Int, m: Int, parallelism: Int, seed: Long = java.time.Instant.now.toEpochMilli): Seq[RegressionTree]
Train an extratrees regression forest
Train an extratrees regression forest
- nMin
minimum sample size for splitting a node
- k
number of features to consider in each split step. The best among these will be chosen.
- m
number of trees
- seed
Returns a list of RegressionTree objects which can be passed to
predictRegression
- def predictClassification(trees: Seq[ClassificationTree], samples: Mat[Double]): Mat[Double]
- def predictRegression(trees: Seq[RegressionTree], samples: Mat[Double]): Vec[Double]
- object ClassificationLeaf extends Serializable
- object ClassificationNonLeaf extends Serializable
- object ClassificationTree
- object RegressionLeaf extends Serializable
- object RegressionNonLeaf extends Serializable
- object RegressionTree