python-3.x - XGBModel'对象没有属性 'evals_result_'

Question

我正在尝试在数据集上使用 xgboost。我在各种博客中看到了相同的语法，但在调用 clf.evals_result() 时遇到错误这是我的代码

from xgboost import XGBRegressor as xgb
from sklearn.metrics import mean_absolute_error as mae

evals_result ={}
eval_s = [(x, y),(xval,yval)]

clf = xgb(n_estimators=100,learning_rate=0.03,tree_method='gpu_hist',lamda=0.1,eval_metric='mae',eval_set=eval_s,early_stopping_rounds=0,evals_result=evals_result)

clf.fit(x,y) 

r = clf.evals_result()

这是我收到的错误

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-138-2d6867968043> in <module>
      1 
----> 2 r = clf.evals_result()
      3 
      4 p = clf.predict(xval)

/opt/conda/lib/python3.6/site-packages/xgboost/sklearn.py in evals_result(self)
    399          'validation_1': {'logloss': ['0.41965', '0.17686']}}
    400         """
--> 401         if self.evals_result_:
    402             evals_result = self.evals_result_
    403         else:

AttributeError: 'XGBRegressor' object has no attribute 'evals_result_'

Answer 1

我得到了完全相同的错误，解决方案是将 eval_set 传递给 fit 函数，而不是在创建分类器时

clf.fit(x,y,eval_set=eval_s) 

然后就可以运行 clf.evals_result()