case class MultiTaskTreeLearner(numFeatures: Int = -1, maxDepth: Int = 30, minLeafInstances: Int = 1, randomizePivotLocation: Boolean = false, rng: Random = Random) extends MultiTaskLearner with Product with Serializable
A tree learner that operates on multiple labels.
- numFeatures
to random select from at each split (numbers less than 0 indicate that all features are used)
- maxDepth
to grow the tree to
- minLeafInstances
minimum number of training instances per leaf
- randomizePivotLocation
whether to generate splits randomly between the data points
- rng
random number generator, for reproducibility
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- new MultiTaskTreeLearner(numFeatures: Int = -1, maxDepth: Int = 30, minLeafInstances: Int = 1, randomizePivotLocation: Boolean = false, rng: Random = Random)
- numFeatures
to random select from at each split (numbers less than 0 indicate that all features are used)
- maxDepth
to grow the tree to
- minLeafInstances
minimum number of training instances per leaf
- randomizePivotLocation
whether to generate splits randomly between the data points
- rng
random number generator, for reproducibility
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- val maxDepth: Int
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- val numFeatures: Int
- def productElementNames: Iterator[String]
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- val randomizePivotLocation: Boolean
- val rng: Random
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- def train(trainingData: Seq[(Vector[Any], Vector[Any])], weights: Option[Seq[Double]]): MultiTaskTreeTrainingResult
Construct one regression or classification tree for each label.
Construct one regression or classification tree for each label.
- trainingData
to train on
- weights
for the training rows, if applicable
- returns
sequence of models, one for each label
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- MultiTaskTreeLearner → MultiTaskLearner
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