case class RandomForest(numTrees: Int = -1, useJackknife: Boolean = true, biasLearner: Option[Learner] = None, leafLearner: Option[Learner] = None, subsetStrategy: Any = "auto") extends Learner with Product with Serializable
Standard random forest as a wrapper around bagged decision trees Created by maxhutch on 1/9/17.
- numTrees
number of trees to use (-1 => number of training instances)
- useJackknife
whether to use jackknife based variance estimates
- biasLearner
learner to model bias (absolute residual)
- leafLearner
learner to use at the leaves of the trees
- subsetStrategy
for random feature selection at each split (auto => 1/3 for regression, sqrt for classification)
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RandomForest(numTrees: Int = -1, useJackknife: Boolean = true, biasLearner: Option[Learner] = None, leafLearner: Option[Learner] = None, subsetStrategy: Any = "auto")
- numTrees
number of trees to use (-1 => number of training instances)
- useJackknife
whether to use jackknife based variance estimates
- biasLearner
learner to model bias (absolute residual)
- leafLearner
learner to use at the leaves of the trees
- subsetStrategy
for random feature selection at each split (auto => 1/3 for regression, sqrt for classification)
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- val biasLearner: Option[Learner]
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- val leafLearner: Option[Learner]
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- val numTrees: Int
- val subsetStrategy: Any
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def
train(trainingData: Seq[(Vector[Any], Any)], weights: Option[Seq[Double]]): TrainingResult
Train a random forest model
Train a random forest model
If the training labels are Doubles, this is a regression forest; otherwise, a classification forest. Options like the number of trees are set via setHyper
- trainingData
to train on
- weights
for the training rows, if applicable
- returns
training result containing a model
- Definition Classes
- RandomForest → 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
- Definition Classes
- Learner
- val useJackknife: Boolean
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