case class FeatureRotator(baseLearner: Learner) extends Learner with Product with Serializable
Rotate the training data before passing along to a base learner
This may be useful for improving randomization in random forests, especially when using random feature selection without bagging.
Created by gregor-robinson on 2020-01-02.
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- def train(trainingData: Seq[(Vector[Any], Any)], weights: Option[Seq[Double]]): RotatedFeatureTrainingResult
Create linear transformations for continuous features and labels & pass data through to learner
Create linear transformations for continuous features and labels & pass data through to learner
- trainingData
to train on
- weights
for the training rows, if applicable
- returns
training result containing a model
- Definition Classes
- FeatureRotator → Learner
- def train(trainingData: Seq[(Vector[Any], Any, Double)]): TrainingResult
Train a model with weights
Train a model with weights
- trainingData
with weights in the form (features, label, weight)
- returns
training result containing a model
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- Learner
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