Type Members

1.  case class BaggedClassificationResult(predictions: Seq[PredictionResult[Any]]) extends BaggedResult[Any] with Product with Serializable
2.  class BaggedModel[+T] extends Model[BaggedResult[T]]

   Container holding a parallel sequence of models and the sample counts used to train them

3.  case class BaggedMultiResult(predictions: Seq[PredictionResult[Double]], NibIn: Vector[Vector[Int]], bias: Option[Seq[Double]] = None, rescale: Double = 1.0) extends BaggedResult[Double] with RegressionResult with Product with Serializable

   Container with model-wise predictions and logic to compute variances and training row scores

   Container with model-wise predictions and logic to compute variances and training row scores

   These calculations are implemented using matrix arithmetic to make them more performant when the number of predictions is large. This obfuscates the algorithm significantly, however. To see what is being computed, look at BaggedSingleResult, which is more clear. These two implementations are tested for consistency.

   predictions

   for each constituent model

   NibIn

   the sample matrix as (N_models x N_training)

   bias

   model to use for estimating bias

4.  trait BaggedResult[+T] extends PredictionResult[T]

   Interface defining the return value of a BaggedModel

   Interface defining the return value of a BaggedModel

   This allows the implementation to depend on the number of simultaneous predictions, which has performance implications.

5.  case class BaggedSingleResult(predictions: Seq[PredictionResult[Double]], NibIn: Vector[Vector[Int]], bias: Option[Double] = None, rescale: Double = 1.0) extends BaggedResult[Double] with RegressionResult with Product with Serializable

   Container with model-wise predictions and logic to compute variances and training row scores

   Container with model-wise predictions and logic to compute variances and training row scores

   predictions

   for each constituent model

   NibIn

   the sample matrix as (N_models x N_training)

   bias

   model to use for estimating bias

6.  class BaggedTrainingResult[+T] extends TrainingResult
7.  case class Bagger(method: Learner, numBags: Int = -1, useJackknife: Boolean = true, biasLearner: Option[Learner] = None, uncertaintyCalibration: Boolean = false) extends Learner with Product with Serializable

   A bagger creates an ensemble of models by training the learner on random samples of the training data

   A bagger creates an ensemble of models by training the learner on random samples of the training data

   Created by maxhutch on 11/14/16.

   method

   learner to train each model in the ensemble

   numBags

   number of models in the ensemble

8.  case class BaggerHelper(models: ParSeq[Model[PredictionResult[Any]]], trainingData: Seq[(Vector[Any], Any)], Nib: Vector[Vector[Int]], useJackknife: Boolean, uncertaintyCalibration: Boolean) extends Product with Serializable

   Helper class to subsume shared functionality of Bagger and MultiTaskBagger.

   Helper class to subsume shared functionality of Bagger and MultiTaskBagger.

   models

   collection of trained models

   trainingData

   on which models were trained

   Nib

   vector (over models) of vectors (over training data) of the number of repeats in each model's bag

   useJackknife

   whether to use jackknife for uncertainty quantification

   uncertaintyCalibration

   whether to apply empirical uncertainty calibration

   Attributes

   protected

9.  case class MultiTaskBagger(method: MultiTaskLearner, numBags: Int = -1, useJackknife: Boolean = true, biasLearner: Option[Learner] = None, uncertaintyCalibration: Boolean = false) extends MultiTaskLearner with Product with Serializable

   Create an ensemble of multi-task models

   Create an ensemble of multi-task models

   method

   learner to train each model in the ensemble

   numBags

   number of models in the ensemble