case class Standardizer(baseLearner: Learner) extends Learner with Product with Serializable
Standardize the training data to zero mean and unit variance before feeding it into another learner
This is particularly helpful for regularized methods, like ridge regression, where the relative scale of the features and labels is important.
Created by maxhutch on 2/19/17.
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def
train(trainingData: Seq[(Vector[Any], Any)], weights: Option[Seq[Double]]): StandardizerTrainingResult
Create affine transformations for continuous features and labels; pass data through to learner
Create affine 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
- Standardizer → Learner
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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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