随机森林（用的jupyter lab）调参问题

Question

在随机森林调参中（用的jupyter lab），出现了错误，以下是代码

scorel = []
for i in range(0,200,20):
    rfc = RandomForestClassifier(n_estimators=i+1,
                                 n_jobs=-1,
                                 random_state=90)
    score = cross_val_score(rfc,data.data,data.target,cv=10).mean()
    scorel.append(score)
print(max(scorel),(scorel.index(max(scorel))*10)+1)
plt.figure(figsize=[20,5])
plt.plot(range(1,201,10),scorel)
plt.show()

提示错误：
D:\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:372: FitFailedWarning: 
10 fits failed out of a total of 10.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
10 fits failed with the following error:
Traceback (most recent call last):
  File "D:\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 680, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "D:\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 450, in fit
    trees = Parallel(
  File "D:\Anaconda3\lib\site-packages\joblib\parallel.py", line 968, in __call__
    n_jobs = self._initialize_backend()
  File "D:\Anaconda3\lib\site-packages\joblib\parallel.py", line 735, in _initialize_backend
    n_jobs = self._backend.configure(n_jobs=self.n_jobs, parallel=self,
  File "D:\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 494, in configure
    self._workers = get_memmapping_executor(
  File "D:\Anaconda3\lib\site-packages\joblib\executor.py", line 20, in get_memmapping_executor
    return MemmappingExecutor.get_memmapping_executor(n_jobs, **kwargs)
  File "D:\Anaconda3\lib\site-packages\joblib\executor.py", line 42, in get_memmapping_executor
    manager = TemporaryResourcesManager(temp_folder)
  File "D:\Anaconda3\lib\site-packages\joblib\_memmapping_reducer.py", line 531, in __init__
    self.set_current_context(context_id)
  File "D:\Anaconda3\lib\site-packages\joblib\_memmapping_reducer.py", line 535, in set_current_context
    self.register_new_context(context_id)
  File "D:\Anaconda3\lib\site-packages\joblib\_memmapping_reducer.py", line 560, in register_new_context
    self.register_folder_finalizer(new_folder_path, context_id)
  File "D:\Anaconda3\lib\site-packages\joblib\_memmapping_reducer.py", line 590, in register_folder_finalizer
    resource_tracker.register(pool_subfolder, "folder")
  File "D:\Anaconda3\lib\site-packages\joblib\externals\loky\backend\resource_tracker.py", line 191, in register
    self._send('REGISTER', name, rtype)
  File "D:\Anaconda3\lib\site-packages\joblib\externals\loky\backend\resource_tracker.py", line 204, in _send
    msg = '{0}:{1}:{2}\n'.format(cmd, name, rtype).encode('ascii')
UnicodeEncodeError: 'ascii' codec can't encode characters in position 18-20: ordinal not in range(128)

  warnings.warn(some_fits_failed_message, FitFailedWarning)
D:\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:372: FitFailedWarning: 
10 fits failed out of a total of 10.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:




这个是哪里的问题，该怎么解决

Answer 1

以下回复参考：皆我百晓生、券券喵儿等免费微信小程序作答:

这个问题是由于在运行随机森林调参时，某些参数组合导致了拟合失败。这可能是由于数据集的某些特性导致的。为了解决这个问题，你可以尝试以下方法：

1. 增加n_jobs的值。这将允许更多的并行任务执行，可能会加快拟合过程并减少失败的可能性。例如，将n_jobs=-1改为n_jobs=4（根据你的计算机的核心数量进行调整）。

2. 使用error_score='raise'来查看具体的错误信息。这将在拟合失败时引发异常，从而帮助你了解失败的原因。你可以在cross_val_score函数中添加这个参数，如下所示：

from sklearn.exceptions import FitFailedWarning
import warnings

warnings.filterwarnings("ignore", category=FitFailedWarning)

scorel = []
for i in range(0, 200, 20):
    rfc = RandomForestClassifier(n_estimators=i + 1,
                                 n_jobs=-1,
                                 random_state=90)
    try:
        score = cross_val_score(rfc, data.data, data.target, cv=10, error_score='raise').mean()
        scorel.append(score)
    except Exception as e:
        print(f"Error at n_estimators={i + 1}: {e}")
        scorel.append(np.nan)

print(max(scorel), (scorel.index(max(scorel)) * 10) + 1)
plt.figure(figsize=[20, 5])
plt.plot(range(1, 201, 10), scorel)
plt.show()

1. 如果上述方法仍然无法解决问题，你可以尝试调整其他参数，如max_depth、min_samples_split等，以找到更适合你的数据集的参数组合。同时，确保你的数据集已经进行了适当的预处理，例如缺失值填充、特征缩放等。