Class: LightGBM::Regressor
Instance Attribute Summary
Attributes inherited from Model
Instance Method Summary collapse
- #fit(x, y, categorical_feature: "auto", eval_set: nil, eval_names: [], early_stopping_rounds: nil, verbose: true) ⇒ Object
-
#initialize(num_leaves: 31, learning_rate: 0.1, n_estimators: 100, objective: "regression", **options) ⇒ Regressor
constructor
A new instance of Regressor.
- #predict(data, num_iteration: nil) ⇒ Object
Methods inherited from Model
#best_iteration, #feature_importances, #load_model, #save_model
Constructor Details
#initialize(num_leaves: 31, learning_rate: 0.1, n_estimators: 100, objective: "regression", **options) ⇒ Regressor
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# File 'lib/lightgbm/regressor.rb', line 3 def initialize(num_leaves: 31, learning_rate: 0.1, n_estimators: 100, objective: "regression", **) super end |
Instance Method Details
#fit(x, y, categorical_feature: "auto", eval_set: nil, eval_names: [], early_stopping_rounds: nil, verbose: true) ⇒ Object
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# File 'lib/lightgbm/regressor.rb', line 7 def fit(x, y, categorical_feature: "auto", eval_set: nil, eval_names: [], early_stopping_rounds: nil, verbose: true) train_set = Dataset.new(x, label: y, categorical_feature: categorical_feature, params: @params) valid_sets = Array(eval_set).map { |v| Dataset.new(v[0], label: v[1], reference: train_set, params: @params) } @booster = LightGBM.train(@params, train_set, num_boost_round: @n_estimators, early_stopping_rounds: early_stopping_rounds, verbose_eval: verbose, valid_sets: valid_sets, valid_names: eval_names ) nil end |
#predict(data, num_iteration: nil) ⇒ Object
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# File 'lib/lightgbm/regressor.rb', line 21 def predict(data, num_iteration: nil) @booster.predict(data, num_iteration: num_iteration) end |