class LightGBM::Regressor
Public Class Methods
new(num_leaves: 31, learning_rate: 0.1, n_estimators: 100, objective: "regression", **options)
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Calls superclass method
LightGBM::Model::new
# File lib/lightgbm/regressor.rb, line 3 def initialize(num_leaves: 31, learning_rate: 0.1, n_estimators: 100, objective: "regression", **options) super end
Public Instance Methods
fit(x, y, categorical_feature: "auto", eval_set: nil, eval_names: [], early_stopping_rounds: nil, verbose: true)
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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)
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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