cannot import name 'KNeighborsClassifie' from 'sklearn.neighbors' (C:\ProgramData\anaconda3\lib\site-packages\sklearn\neighbors\__init__.py)

这个错误通常是由于导入模块时出现了拼写错误或版本不匹配的问题导致的。在你的问题中，你尝试导入的是 'KNeighborsClassifie'，而正确的类名应为 'KNeighborsClassifier'。请确保你正确地拼写了模块和类名，并且你安装的 scikit-learn 库的版本是最新的。

你可以尝试以下步骤来解决这个问题：
1. 检查拼写错误：确认你在代码中正确地拼写了模块和类名。
2. 更新 scikit-learn：运行 `pip install -U scikit-learn` 命令来更新你的 scikit-learn 库到最新版本。
3. 检查环境：确保你正在使用正确的 Python 环境，并且没有其他库或模块与 scikit-learn 发生冲突。

如果问题仍然存在，你可以提供更多的代码和错误信息，以便我能够更准确地帮助你解决这个问题。

相关问题

cannot import name 'KNeighborsClassfier' from 'sklearn.neighbors' (E:\Anaconda3\lib\site-packages\sklearn\neighbors\__init__.py)

这个问题是因为在尝试从`sklearn.neighbors`模块导入`KNeighborsClassifier`时，出现了错误。正确的类名应该是`KNeighborsClassifier`，而不是`KNeighborsClassfier`。请检查并修改代码中的拼写错误。

正确的导入方式如下：

from sklearn.neighbors import KNeighborsClassifier

优化这段代码# import modules 导入模块 from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA import pandas as pd from sklearn import svm import numpy as np import math import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import colors from sklearn.model_selection import train_test_split from sklearn import datasets from matplotlib.colors import ListedColormap from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler from sklearn.model_selection import StratifiedShuffleSplit,StratifiedKFold from sklearn.model_selection import GridSearchCV from sklearn.model_selection import GridSearchCV, LeaveOneOut, cross_val_predict from sklearn.model_selection import KFold from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier from sklearn import svm from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import roc_auc_score from sklearn.metrics import roc_auc_score import math import datetime import multiprocessing as mp from sklearn.ensemble import StackingClassifier from sklearn.pipeline import make_pipeline from sklearn.svm import LinearSVC import random

你可以对这段代码进行如下优化：

import pandas as pd
import numpy as np
import math
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib import colors
from matplotlib.colors import ListedColormap
from sklearn.model_selection import train_test_split, StratifiedShuffleSplit, StratifiedKFold, GridSearchCV, LeaveOneOut, cross_val_predict, KFold
from sklearn.decomposition import PCA
from sklearn.svm import SVC, LinearSVC
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, StackingClassifier
from sklearn.metrics import roc_auc_score

# 导入模块

# 设置随机种子
random.seed(42)
np.random.seed(42)

# 其他代码...

这样做的优点是将导入的模块进行整理和分类，提高了代码的可读性。同时，设置了随机种子，保证了代码的可复现性。你可以根据需要添加其他的代码或模块。