Package smile.classification

Record Class RandomForest.Options

java.lang.Object
java.lang.Record
smile.classification.RandomForest.Options
Record Components:
ntrees - the number of trees.
mtry - the number of input variables to be used to determine the decision at a node of the tree. p/3 generally give good performance, where p is the number of variables.
rule - Decision tree split rule.
maxDepth - the maximum depth of the tree.
maxNodes - the maximum number of leaf nodes in the tree.
nodeSize - the minimum size of leaf nodes. Setting nodeSize = 5 generally gives good results.
subsample - the sampling rate for training tree. 1.0 means sampling with replacement. < 1.0 means sampling without replacement.
classWeight - Priors of the classes. The weight of each class is roughly the ratio of samples in each class. For example, if there are 400 positive samples and 100 negative samples, the classWeight should be [1, 4] (assuming label 0 is of negative, label 1 is of positive).
seeds - optional RNG seeds for each decision tree.
controller - the optional training controller.
Enclosing class:
RandomForest
public static record RandomForest.Options(int ntrees, int mtry, SplitRule rule, int maxDepth, int maxNodes, int nodeSize, double subsample, int[] classWeight, long[] seeds, IterativeAlgorithmController<RandomForest.TrainingStatus> controller) extends Record
Random forest hyperparameters.

  • Constructor Details

    • Options

      public Options(int ntrees, int mtry, SplitRule rule, int maxDepth, int maxNodes, int nodeSize, double subsample, int[] classWeight, long[] seeds, IterativeAlgorithmController<RandomForest.TrainingStatus> controller)
      Constructor.

    • Options

      public Options(int ntrees)
      Constructor.
      Parameters:
      ntrees - the number of trees.

    • Options

      public Options(int ntrees, int mtry, int maxDepth, int maxNodes, int nodeSize)
      Constructor.
      Parameters:
      ntrees - the number of trees.
      mtry - the number of input variables to be used to determine the decision at a node of the tree. p/3 generally give good performance, where p is the number of variables.
      maxDepth - the maximum depth of the tree.
      maxNodes - the maximum number of leaf nodes in the tree.
      nodeSize - the minimum size of leaf nodes. Setting nodeSize = 5 generally gives good results.

  • Method Details

    • toProperties

      public Properties toProperties()
      Returns the persistent set of hyperparameters.
      Returns:
      the persistent set.

    • of

      public static RandomForest.Options of(Properties props)
      Returns the options from properties.
      Parameters:
      props - the hyperparameters.
      Returns:
      the options.

    • toString

      public final String toString()
      Returns a string representation of this record class. The representation contains the name of the class, followed by the name and value of each of the record components.
      Specified by:
      toString in class Record
      Returns:
      a string representation of this object

    • hashCode

      public final int hashCode()
      Returns a hash code value for this object. The value is derived from the hash code of each of the record components.
      Specified by:
      hashCode in class Record
      Returns:
      a hash code value for this object

    • equals

      public final boolean equals(Object o)
      Indicates whether some other object is "equal to" this one. The objects are equal if the other object is of the same class and if all the record components are equal. Reference components are compared with Objects::equals(Object,Object); primitive components are compared with '=='.
      Specified by:
      equals in class Record
      Parameters:
      o - the object with which to compare
      Returns:
      true if this object is the same as the o argument; false otherwise.

    • ntrees

      public int ntrees()
      Returns the value of the ntrees record component.
      Returns:
      the value of the ntrees record component

    • mtry

      public int mtry()
      Returns the value of the mtry record component.
      Returns:
      the value of the mtry record component

    • rule

      public SplitRule rule()
      Returns the value of the rule record component.
      Returns:
      the value of the rule record component

    • maxDepth

      public int maxDepth()
      Returns the value of the maxDepth record component.
      Returns:
      the value of the maxDepth record component

    • maxNodes

      public int maxNodes()
      Returns the value of the maxNodes record component.
      Returns:
      the value of the maxNodes record component

    • nodeSize

      public int nodeSize()
      Returns the value of the nodeSize record component.
      Returns:
      the value of the nodeSize record component

    • subsample

      public double subsample()
      Returns the value of the subsample record component.
      Returns:
      the value of the subsample record component

    • classWeight

      public int[] classWeight()
      Returns the value of the classWeight record component.
      Returns:
      the value of the classWeight record component

    • seeds

      public long[] seeds()
      Returns the value of the seeds record component.
      Returns:
      the value of the seeds record component

    • controller

      public IterativeAlgorithmController<RandomForest.TrainingStatus> controller()
      Returns the value of the controller record component.
      Returns:
      the value of the controller record component