case class MultiTaskBagger(method: MultiTaskLearner, numBags: Int = -1, useJackknife: Boolean = true, biasLearner: Option[Learner] = None, uncertaintyCalibration: Boolean = true, randBasis: RandBasis = Rand) extends MultiTaskLearner with Product with Serializable
Create an ensemble of multi-task models
- method
learner to train each model in the ensemble
- numBags
number of models in the ensemble
- useJackknife
whether to enable jackknife uncertainty estimate
- biasLearner
learner to use for estimating bias
- uncertaintyCalibration
whether to empirically recalibrate the predicted uncertainties
- randBasis
breeze RandBasis to use for generating breeze random numbers
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- new MultiTaskBagger(method: MultiTaskLearner, numBags: Int = -1, useJackknife: Boolean = true, biasLearner: Option[Learner] = None, uncertaintyCalibration: Boolean = true, randBasis: RandBasis = Rand)
- method
learner to train each model in the ensemble
- numBags
number of models in the ensemble
- useJackknife
whether to enable jackknife uncertainty estimate
- biasLearner
learner to use for estimating bias
- uncertaintyCalibration
whether to empirically recalibrate the predicted uncertainties
- randBasis
breeze RandBasis to use for generating breeze random numbers
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- val biasLearner: Option[Learner]
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- def combineImportance(v1: Option[Vector[Double]], v2: Option[Vector[Double]]): Option[Vector[Double]]
Combine two optional feature importance vectors.
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- val method: MultiTaskLearner
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- val numBags: Int
- def productElementNames: Iterator[String]
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- Product
- val randBasis: RandBasis
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- def train(trainingData: Seq[(Vector[Any], Vector[Any])], weights: Option[Seq[Double]] = None): MultiTaskBaggedTrainingResult
Train a model
Train a model
- trainingData
to train on. Each entry is a tuple (vector of inputs, vector of labels)
- weights
for the training rows, if applicable
- returns
A training result that encompasses model(s) for all labels.
- Definition Classes
- MultiTaskBagger → MultiTaskLearner
- val uncertaintyCalibration: Boolean
- val useJackknife: Boolean
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