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extratrees

package extratrees

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Type Members

  1. case class ClassificationLeaf(targetDistribution: Seq[Double]) extends ClassificationTree with Product with Serializable
  2. case class ClassificationNonLeaf(left: ClassificationTree, right: ClassificationTree, splitFeature: Int, cutpoint: Double) extends ClassificationTree with Product with Serializable
  3. sealed trait ClassificationTree extends AnyRef
  4. case class RegressionLeaf(targetMean: Double) extends RegressionTree with Product with Serializable
  5. case class RegressionNonLeaf(left: RegressionTree, right: RegressionTree, splitFeature: Int, cutpoint: Double) extends RegressionTree with Product with Serializable
  6. sealed trait RegressionTree extends AnyRef

Value Members

  1. 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]
  2. 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]
  3. def buildTreeClassification(data: Mat[Double], subset: Vec[Int], target: Vec[Int], sampleWeights: Option[Vec[Double]], nMin: Int, k: Int, rng: Generator, numClasses: Int, attributes: Array[Int], numConstant: Int): ClassificationTree
  4. def buildTreeRegression(data: Mat[Double], subset: Vec[Int], target: Vec[Double], nMin: Int, k: Int, rng: Generator, attributes: Array[Int], numConstant: Int): RegressionTree
  5. def col(data: Mat[Double], col: Int): Vec[Double]
  6. def computeVarianceReduction(target: Vec[Double], samplesInSplit: Vec[Boolean], varianceNoSplit: Double): Double
  7. def distribution(v: Vec[Int], sampleWeights: Option[Vec[Double]], numClasses: Int): Vec[Double]
  8. def giniImpurity(target: Vec[Int], weights: Option[Vec[Double]], numClasses: Int): Double
  9. def giniImpurityFromDistribution(distribution: Array[Double]): Double
  10. def giniScore(target: Vec[Int], sampleWeights: Option[Vec[Double]], samplesInSplit: Vec[Boolean], giniImpurityNoSplit: Double, numClasses: Int, buf1: Array[Double], buf2: Array[Double]): Double
  11. def minmax(self: Vec[Double]): (Double, Double)
  12. def partition[T](vec: Vec[T])(pred: Array[Boolean])(implicit arg0: ClassTag[T]): (Vec[T], Vec[T])
  13. def predictClassification(trees: Seq[ClassificationTree], samples: Mat[Double]): Mat[Double]
  14. def predictClassification(root: ClassificationTree, sample: Vec[Double]): Vec[Double]
  15. def predictRegression(trees: Seq[RegressionTree], samples: Mat[Double]): Vec[Double]
  16. def predictRegression(root: RegressionTree, sample: Vec[Double]): Double
  17. def splitClassification(data: Mat[Double], subset: Vec[Int], attributes: Array[Int], numConstant: Int, k: Int, targetAtSubset: Vec[Int], weightsAtSubset: Option[Vec[Double]], rng: Generator, numClasses: Int): (Int, Double, Int)
  18. def splitRegression(data: Mat[Double], subset: Vec[Int], attributes: Array[Int], numConstant: Int, k: Int, targetAtSubset: Vec[Double], rng: Generator): (Int, Double, Int)
  19. def takeCol(data: Mat[Double], rows: Vec[Int], col: Int): Vec[Double]
  20. object ClassificationLeaf extends Serializable
  21. object ClassificationNonLeaf extends Serializable
  22. object ClassificationTree
  23. object RegressionLeaf extends Serializable
  24. object RegressionNonLeaf extends Serializable
  25. object RegressionTree

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