def compile_model(network):
    '\n    :param network dict: dictionary with network parameters\n    :return: compiled model\n    '
    model = lgb.LGBMRegressor(num_leaves=network.get('num_leaves', 31), learning_rate=network.get('learning_rate', 0.1), n_estimators=network.get('n_estimators', 20), max_bin=network.get('max_bin', 1000), colsample_bytree=network.get('colsample_bytree', 0.5), subsample_for_bin=network.get('subsample_for_bin', 200000), boosting_type=network.get('boosting_type', 'gbdt'), num_iterations=network.get('num_iterations', 100), extra_trees=network.get('extra_trees', False), reg_sqrt=network.get('reg_sqrt', False), bagging_freq=network.get('bagging_freq', 1), bagging_fraction=network.get('bagging_fraction', 0.1))
    return model
:param network dict: dictionary with network parameters
:return: compiled model
def compile_model(network):
    '\n    :param network dict: dictionary with network parameters\n    :return: compiled model\n    '
    model = lgb.LGBMRegressor(num_leaves=network.get('num_leaves', 31), learning_rate=network.get('learning_rate', 0.1), n_estimators=network.get('n_estimators', 20), max_bin=network.get('max_bin', 1000), colsample_bytree=network.get('colsample_bytree', 0.5), subsample_for_bin=network.get('subsample_for_bin', 200000), boosting_type=network.get('boosting_type', 'gbdt'), num_iterations=network.get('num_iterations', 100), extra_trees=network.get('extra_trees', False), reg_sqrt=network.get('reg_sqrt', False), bagging_freq=network.get('bagging_freq', 1), bagging_fraction=network.get('bagging_fraction', 0.1))
    return model