title: how can I fix this WARNING in Xgboost?
body: <p>I have an imbalanced dataset with 53987 rows, 32columns and 8 classes. I'm trying to perform multiclass classification. This is my code and the corresponding output:</p>
  <pre><code>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
  </code></pre>
  <p>and</p>
  <pre><code>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.
  </code></pre>
  <p>How can I fix these warnings?</p>