Type Members

1.  case class ExtraRandomTrees(numTrees: Int = -1, useJackknife: Boolean = false, biasLearner: Option[Learner] = None, leafLearner: Option[Learner] = None, subsetStrategy: Any = "auto", minLeafInstances: Int = 1, maxDepth: Int = Integer.MAX_VALUE, uncertaintyCalibration: Boolean = false, disableBootstrap: Boolean = true, randomlyRotateFeatures: Boolean = false, rng: Random = Random) extends Learner with Product with Serializable

   Extremely randomized tree ensemble

   Extremely randomized tree ensemble

   This is based on Geurts, P., Ernst, D. & Wehenkel, L. Extremely randomized trees. Mach Learn 63, 3–42 (2006). https://doi.org/10.1007/s10994-006-6226-1.

   numTrees

   number of trees to use (-1 => number of training instances)

   useJackknife

   whether to use jackknife based variance estimates

   biasLearner

   learner to model bias (absolute residual)

   leafLearner

   learner to use at the leaves of the trees

   subsetStrategy

   for random feature selection at each split (auto => all features for regression)

   minLeafInstances

   minimum number of instances per leave in each tree

   maxDepth

   maximum depth of each tree in the forest (default: unlimited)

   uncertaintyCalibration

   whether to empirically recalibrate the predicted uncertainties (default: false)

   disableBootstrap

   whether to disable bootstrap (default: true)

   randomlyRotateFeatures

   whether to randomly rotate real features for each tree in the forest (default: false)

   rng

   random number generator to use

2.  case class RandomForest(numTrees: Int = -1, useJackknife: Boolean = true, biasLearner: Option[Learner] = None, leafLearner: Option[Learner] = None, subsetStrategy: Any = "auto", minLeafInstances: Int = 1, maxDepth: Int = Integer.MAX_VALUE, uncertaintyCalibration: Boolean = true, randomizePivotLocation: Boolean = false, randomlyRotateFeatures: Boolean = false, rng: Random = Random) extends Learner with Product with Serializable

   Standard random forest as a wrapper around bagged decision trees Created by maxhutch on 1/9/17.

   Standard random forest as a wrapper around bagged decision trees Created by maxhutch on 1/9/17.

   numTrees

   number of trees to use (-1 => number of training instances)

   useJackknife

   whether to use jackknife based variance estimates

   biasLearner

   learner to model bias (absolute residual)

   leafLearner

   learner to use at the leaves of the trees

   subsetStrategy

   for random feature selection at each split (auto => all fetures for regression, sqrt for classification)

   minLeafInstances

   minimum number of instances per leave in each tree

   maxDepth

   maximum depth of each tree in the forest (default: unlimited)

   uncertaintyCalibration

   whether to empirically recalibrate the predicted uncertainties (default: false)

   randomizePivotLocation

   whether to generate splits randomly between the data points (default: false)

   randomlyRotateFeatures

   whether to randomly rotate real features for each tree in the forest (default: false)

   rng

   random number generator to use for stochastic functionality