class LightGBM::Regressor

Public Class Methods

new(num_leaves: 31, learning_rate: 0.1, n_estimators: 100, objective: "regression", **options) click to toggle source
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) click to toggle source
# 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) click to toggle source
# File lib/lightgbm/regressor.rb, line 21
def predict(data, num_iteration: nil)
  @booster.predict(data, num_iteration: num_iteration)
end