Class: Rumale::Tree::ExtraTreeRegressor

Inherits:
DecisionTreeRegressor show all
Defined in:
lib/rumale/tree/extra_tree_regressor.rb

Overview

ExtraTreeRegressor is a class that implements extra randomized tree for regression.

Reference

    1. Geurts, D. Ernst, and L. Wehenkel, “Extremely randomized trees,” Machine Learning, vol. 63 (1), pp. 3–42, 2006.

Examples:

estimator =
  Rumale::Tree::ExtraTreeRegressor.new(
    max_depth: 3, max_leaf_nodes: 10, min_samples_leaf: 5, random_seed: 1)
estimator.fit(training_samples, traininig_values)
results = estimator.predict(testing_samples)

Instance Attribute Summary collapse

Attributes included from Base::BaseEstimator

#params

Instance Method Summary collapse

Methods included from Base::Regressor

#score

Methods inherited from BaseDecisionTree

#apply

Constructor Details

#initialize(criterion: 'mse', max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1, max_features: nil, random_seed: nil) ⇒ ExtraTreeRegressor

Create a new regressor with extra randomized tree algorithm.



47
48
49
50
51
52
53
54
55
56
 
# File 'lib/rumale/tree/extra_tree_regressor.rb', line 47

def initialize(criterion: 'mse', max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1, max_features: nil,
               random_seed: nil)
  check_params_type_or_nil(Integer, max_depth: max_depth, max_leaf_nodes: max_leaf_nodes,
                                    max_features: max_features, random_seed: random_seed)
  check_params_integer(min_samples_leaf: min_samples_leaf)
  check_params_string(criterion: criterion)
  check_params_positive(max_depth: max_depth, max_leaf_nodes: max_leaf_nodes,
                        min_samples_leaf: min_samples_leaf, max_features: max_features)
  super
end

Instance Attribute Details

#feature_importances ⇒ Numo::DFloat (readonly)

Return the importance for each feature.



21
22
23
 
# File 'lib/rumale/tree/extra_tree_regressor.rb', line 21

def feature_importances
  @feature_importances
end

#leaf_values ⇒ Numo::DFloat (readonly)

Return the values assigned each leaf.



33
34
35
 
# File 'lib/rumale/tree/extra_tree_regressor.rb', line 33

def leaf_values
  @leaf_values
end

#rng ⇒ Random (readonly)

Return the random generator for random selection of feature index.



29
30
31
 
# File 'lib/rumale/tree/extra_tree_regressor.rb', line 29

def rng
  @rng
end

#tree ⇒ Node (readonly)

Return the learned tree.



25
26
27
 
# File 'lib/rumale/tree/extra_tree_regressor.rb', line 25

def tree
  @tree
end

Instance Method Details

#fit(x, y) ⇒ ExtraTreeRegressor

Fit the model with given training data.



63
64
65
66
67
68
 
# File 'lib/rumale/tree/extra_tree_regressor.rb', line 63

def fit(x, y)
  check_sample_array(x)
  check_tvalue_array(y)
  check_sample_tvalue_size(x, y)
  super
end

#marshal_dump ⇒ Hash

Dump marshal data.



81
82
83
 
# File 'lib/rumale/tree/extra_tree_regressor.rb', line 81

def marshal_dump
  super
end

#marshal_load(obj) ⇒ nil

Load marshal data.



87
88
89
 
# File 'lib/rumale/tree/extra_tree_regressor.rb', line 87

def marshal_load(obj)
  super
end

#predict(x) ⇒ Numo::DFloat

Predict values for samples.



74
75
76
77
 
# File 'lib/rumale/tree/extra_tree_regressor.rb', line 74

def predict(x)
  check_sample_array(x)
  super
end