python - 如何在 Xgboost 中修复此警​​告？

Question

我有一个包含 53987 行、32 列和 8 个类的不平衡数据集。我正在尝试执行多类分类。这是我的代码和相应的输出:

from sklearn.metrics import classification_report, accuracy_score
import xgboost
xgb_model = xgboost.XGBClassifier(num_class=7, learning_rate=0.1, num_iterations=1000, max_depth=10, feature_fraction=0.7, 
                              scale_pos_weight=1.5, boosting='gbdt', metric='multiclass')
hr_pred = xgb_model.fit(x_train, y_train).predict(x_test)
print(classification_report(y_test, hr_pred))


[10:03:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: 
Parameters: { boosting, feature_fraction, metric, num_iterations, scale_pos_weight } might not be used.

This may not be accurate due to some parameters are only used in language bindings but
passed down to XGBoost core.  Or some parameters are not used but slip through this verification. Please open an issue if you find above cases.

[10:03:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
          precision    recall  f1-score   support

     1.0       0.84      0.92      0.88      8783
     2.0       0.78      0.80      0.79      4588
     3.0       0.73      0.59      0.65      2109
     4.0       1.00      0.33      0.50         3
     5.0       0.42      0.06      0.11       205
     6.0       0.60      0.12      0.20       197
     7.0       0.79      0.44      0.57       143
     8.0       0.74      0.30      0.42       169

accuracy                           0.81     16197
macro avg       0.74      0.45      0.52     16197
weighted avg       0.80      0.81      0.80     16197

和

max_depth_list = [3,5,7,9,10,15,20,25,30]

for max_depth in max_depth_list:
    xgb_model = xgboost.XGBClassifier(max_depth=max_depth, seed=777)
    xgb_pred = xgb_model.fit(x_train, y_train).predict(x_test)
    xgb_f1_score_micro = f1_score(y_test, xgb_pred, average='micro')

    xgb_df = pd.DataFrame({'tree depth':max_depth_list,             
                            'accuracy':xgb_f1_score_micro})
    xgb_df

WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.

如何修复这些警告？

Answer 1

如果您不想更改任何行为，只需设置 eval_metric='mlogloss'如下。

xgb_model = xgboost.XGBClassifier(num_class=7,
                                  learning_rate=0.1,
                                  num_iterations=1000,
                                  max_depth=10,
                                  feature_fraction=0.7, 
                                  scale_pos_weight=1.5,
                                  boosting='gbdt',
                                  metric='multiclass',
                                  eval_metric='mlogloss')

从警告日志，你会知道什么 eval_metric设置以删除警告的算法。大多数情况下 mlogloss或 logloss .