我在使用iris数据集进行LightGBM的多类分类，代码片段如下：

from sklearn import datasetsfrom sklearn.model_selection import train_test_splitimport pandas as pdimport numpy as npfrom time import timefrom sklearn.metrics import r2_score, mean_squared_errorimport lightgbm as lgbiris = datasets.load_iris()df_features = iris.datadf_dependent = iris.targetx_train,x_test,y_train,y_test = train_test_split(df_features,df_dependent,test_size=0.3, random_state=2)params = {    'task' : 'train',    'boosting_type' : 'gbdt',    'objective' : 'multiclass',    'metric' : {'multi_logloss'},    'num_leaves' : 63,    'learning_rate' : 0.1,    'feature_fraction' : 0.9,    'bagging_fraction' : 0.9,    'bagging_freq': 0,    'verbose' : 0,    'num_class' : 3}lgb_train = lgb.Dataset(x_train, y_train)lgb_eval = lgb.Dataset(x_test, y_test, reference=lgb_train)gbm = lgb.train(params,                lgb_train,                num_boost_round=20,                valid_sets=lgb_eval,                early_stopping_rounds=5)print('Save model...')# save model to filegbm.save_model('model.txt')

在model.txt文件中，我期望number_of_trees等于num_boost_round，但实际上我看到的是60棵树，即num_boost_round乘以num_class，这显然是错误的。

为什么会发生这种情况？

回答：

您可以在LightGBM文档中看到以下说明：

注意：在多类分类问题中，LightGBM内部会构建num_class乘以num_iterations棵树