DecisionTree¶
<auto-generated stub>
Static methods¶
def deserialize(encodedTree: Rep[String]): Rep[DecisionTree]
Infix methods¶
def addNode(parent: Rep[Int], isLeft: Rep[Boolean], isLeaf: Rep[Boolean], feature: Rep[Int], threshold: Rep[Double], impurity: Rep[Double], numNodeSamples: Rep[Int]): Rep[Int]
def capacity(): Rep[Int]
def feature(): Rep[ForgeArray[Int]]
def impurity(): Rep[ForgeArray[Double]]
def isLeaf(): Rep[ForgeArray[Boolean]]
def leftChildren(): Rep[ForgeArray[Int]]
def numNodeSamples(): Rep[ForgeArray[Int]]
def numNodes(): Rep[Int]
def pprint(): Rep[Unit]
def predict(testPt: Rep[DenseVector[Double]]): Rep[Tup2[Double,:doc:double]]
def prob(): Rep[ForgeArray[Double]]
def rightChildren(): Rep[ForgeArray[Int]]
def serialize(): Rep[String] For now we use a simple text-format to persist the tree. Eventually, we probably will want to switch to a binary format for efficiency in saving and loading large forests.
def threshold(): Rep[ForgeArray[Double]]
def value(): Rep[ForgeArray[Double]]