Decision Tree

DecisionTreeLearners

Creates an API wrapper for DecisionTrees for pipeline workflow.

Index

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TSML.DecisionTreeLearners.Adaboost — Type.
Adaboost(
  Dict(
    :output => :class,
    :num_iterations => 7
  )
)

Adaboosted decision tree stumps. See DecisionTree.jl's documentation

Hyperparameters:

  • :num_iterations => 7 (number of iterations of AdaBoost)

Implements fit!, transform!

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TSML.DecisionTreeLearners.PrunedTree — Type.
PrunedTree(
  Dict(
    :purity_threshold => 1.0,
    :max_depth => -1,
    :min_samples_leaf => 1,
    :min_samples_split => 2,
    :min_purity_increase => 0.0
  )
)

Decision tree classifier. See DecisionTree.jl's documentation

Hyperparmeters:

  • :purity_threshold => 1.0 (merge leaves having >=thresh combined purity)
  • :max_depth => -1 (maximum depth of the decision tree)
  • :min_samples_leaf => 1 (the minimum number of samples each leaf needs to have)
  • :min_samples_split => 2 (the minimum number of samples in needed for a split)
  • :min_purity_increase => 0.0 (minimum purity needed for a split)

Implements fit!, transform!

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TSML.DecisionTreeLearners.RandomForest — Type.
RandomForest(
  Dict(
    :output => :class,
    :num_subfeatures => 0,
    :num_trees => 10,
    :partial_sampling => 0.7,
    :max_depth => -1
  )
)

Random forest classification. See DecisionTree.jl's documentation

Hyperparmeters:

  • :num_subfeatures => 0 (number of features to consider at random per split)
  • :num_trees => 10 (number of trees to train)
  • :partial_sampling => 0.7 (fraction of samples to train each tree on)
  • :max_depth => -1 (maximum depth of the decision trees)
  • :min_samples_leaf => 1 (the minimum number of samples each leaf needs to have)
  • :min_samples_split => 2 (the minimum number of samples in needed for a split)
  • :min_purity_increase => 0.0 (minimum purity needed for a split)

Implements fit!, transform!

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TSML.TSMLTypes.fit! — Method.
fit!(adaboost::Adaboost, features::T, labels::Vector) where {T<:Union{Vector,Matrix,DataFrame}}

Function to optimize the hyperparameters of Adaboost instance.

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TSML.TSMLTypes.fit! — Method.
fit!(tree::PrunedTree, features::T, labels::Vector) where {T<:Union{Vector,Matrix,DataFrame}}

Function to optimize the hyperparameters of PrunedTree instance.

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TSML.TSMLTypes.fit! — Method.
fit!(forest::RandomForest, features::T, labels::Vector) where {T<:Union{Vector,Matrix,DataFrame}}

Function to optimize the parameters of the RandomForest instance.

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TSML.TSMLTypes.transform! — Method.
transform!(adaboost::Adaboost, features::T) where {T<:Union{Vector,Matrix,DataFrame}}

Function to predict using the optimized hyperparameters of the trained Adaboost instance.

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TSML.TSMLTypes.transform! — Method.
transform!(tree::PrundTree, features::T) where {T<:Union{Vector,Matrix,DataFrame}}

Function to predict using the optimized hyperparameters of the trained PrunedTree instance.

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TSML.TSMLTypes.transform! — Method.
transform!(forest::RandomForest, features::T) where {T<:Union{Vector,Matrix,DataFrame}}

Function to predict using the optimized hyperparameters of the trained RandomForest instance.

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