class CrystalML::Regression::RandomForestRegression

Included Modules

Defined in:

regression/random_forest_regression.cr

Constructors

Instance Method Summary

Instance methods inherited from module CrystalML::Regressor

predict(data : Tensor(Float64, CPU(Float64))) : Tensor(Float64, CPU(Float64))
predict(data : Array(Array(Float64)) | Crysda::DataFrame) : Tensor(Float64, CPU(Float64)) predict

Instance methods inherited from class CrystalML::SupervisedEstimator

fit(data : Tensor(Float64, CPU(Float64)), target : Tensor(Float64, CPU(Float64)))
fit(data : Tensor(Float64, CPU(Float64)) | Array(Array(Float64)) | Crysda::DataFrame, target : Tensor(Float64, CPU(Float64)) | Array(Array(Float64)) | Array(Float64) | Crysda::DataFrame) fit

Constructor Detail

def self.new(n_trees : Int32 = 100, max_depth : Int32 = 10, min_size : Int32 = 2) #

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Instance Method Detail

def fit(data : Tensor(Float64, CPU(Float64)), target : Tensor(Float64, CPU(Float64))) #

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def max_depth : Int32 #

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def max_depth=(max_depth : Int32) #

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def min_size : Int32 #

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def min_size=(min_size : Int32) #

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def n_trees : Int32 #

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def n_trees=(n_trees : Int32) #

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def predict(data : Tensor(Float64, CPU(Float64))) : Tensor(Float64, CPU(Float64)) #

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def trees : Array(DecisionTreeRegression) #

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def trees=(trees : Array(DecisionTreeRegression)) #

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