case class ClassificationTreeLearner(numFeatures: Int = -1, maxDepth: Int = 30, minLeafInstances: Int = 1, leafLearner: Option[Learner] = None, splitter: Splitter[Char] = ClassificationSplitter(), rng: Random = Random) extends Learner with Product with Serializable
Created by maxhutch on 12/2/16.
- numFeatures
subset of features to select splits from
- maxDepth
maximum depth of tree
- minLeafInstances
minimum training instances per node
- leafLearner
to train on leaves
- splitter
used to select splits
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- new ClassificationTreeLearner(numFeatures: Int = -1, maxDepth: Int = 30, minLeafInstances: Int = 1, leafLearner: Option[Learner] = None, splitter: Splitter[Char] = ClassificationSplitter(), rng: Random = Random)
- numFeatures
subset of features to select splits from
- maxDepth
maximum depth of tree
- minLeafInstances
minimum training instances per node
- leafLearner
to train on leaves
- splitter
used to select splits
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- val leafLearner: Option[Learner]
- val maxDepth: Int
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- val numFeatures: Int
- def productElementNames: Iterator[String]
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- val rng: Random
- val splitter: Splitter[Char]
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- def train(trainingData: Seq[(Vector[Any], Any)], weights: Option[Seq[Double]]): ClassificationTrainingResult
Train classification tree
Train classification tree
- trainingData
to train on
- weights
for the training rows, if applicable
- returns
a classification tree
- Definition Classes
- ClassificationTreeLearner → 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
- Definition Classes
- Learner
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