case class RegressionTreeLearner(numFeatures: Int = -1, maxDepth: Int = 30, minLeafInstances: Int = 1, leafLearner: Option[Learner] = None) extends Learner with Product with Serializable
Learner for regression trees
Created by maxhutch on 11/28/16.
- numFeatures
to randomly select from at each split (default: all)
- maxDepth
to grow the tree to
- leafLearner
learner to train the leaves with
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- val leafLearner: Option[Learner]
- val maxDepth: Int
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- val numFeatures: Int
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def
train(trainingData: Seq[(Vector[Any], Any)], weights: Option[Seq[Double]] = None): RegressionTreeTrainingResult
Train the tree by recursively partitioning (splitting) the training data on a single feature
Train the tree by recursively partitioning (splitting) the training data on a single feature
- trainingData
to train on
- weights
for the training rows, if applicable
- returns
a RegressionTree
- Definition Classes
- RegressionTreeLearner → 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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