你给我的代码我运行了以下部分：import numpy as np from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeClassifier, plot_tree from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score, confusion_matrix, ConfusionMatrixDisplay from sklearn.preprocessing import OneHotEncoder from sklearn.compose import ColumnTransformer from sklearn.pipeline import Pipeline # （三）特征工程 # 复制原始数据避免污染 df_processed = df.copy() # 1.删除不参与建模的列 df_processed.drop(columns=['student_id'], inplace=True) # 2.对需要反向编码的列进行处理（数值越小越优 -> 数值越大越优） reverse_cols = ['atth', 'attc', 'mid2'] df_processed[reverse_cols] = 5 - df_processed[reverse_cols] # 3.处理二值列（将sex从1/2转换为0/1） df_processed['sex'] = df_processed['sex'] - 1 # 4.定义特征和标签 X = df_processed.drop(columns=['ecgp']) y = df_processed['ecgp'] # 5.划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # （四）构建预处理管道 # 定义需要独热编码的分类列（排除已处理的二值列） categorical_cols = ['student_age', 'gh', 'ship', 'studyhours', 'readfren', 'readfres', 'attc', 'mid2', 'noteclass', 'listencla', 'cgp'] preprocessor = ColumnTransformer( transformers=[ ('cat', OneHotEncoder(handle_unknown='ignore'), categorical_cols) ], remainder='passthrough' # 保留已处理的二值列（sex, atth） ) # （五）逻辑回归建模 lr_pipe = Pipeline([ ('preprocessor', preprocessor), ('classifier', LogisticRegression(max_iter=1000)) ]) lr_pipe.fit(X_train, y_train) print(f"\n逻辑回归准确率：{lr_pipe.score(X_test, y_test):.2%}") # （六）决策树建模与可视化 dt_pipe = Pipeline([ ('preprocessor', preprocessor), ('classifier', DecisionTreeClassifier(max_depth=3)) ]) dt_pipe.fit(X_train, y_train) print(f"决策树准确率：{dt_pipe.score(X_test, y_test):.2%}") 在运行代码dt_pipe = Pipeline([ ('preprocessor', preprocessor), ('classifier', DecisionTreeClassifier(max_depth=3)) ]) dt_pipe.fit(X_train, y_train) print(f"决策树准确率：{dt_pipe.score(X_test, y_test):.2%}") 时报错Traceback (most recent call last): File "<stdin>", line 1, in <module> File "C:\Users\33584\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\pipeline.py", line 756, in score Xt = transform.transform(Xt) ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\33584\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\utils\_set_output.py", line 157, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\33584\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\compose\_column_transformer.py", line 805, in transform named_transformers = self.named_transformers_ ^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\33584\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\compose\_column_transformer.py", line 484, in named_transformers_ return Bunch(**{name: trans for name, trans, _ in self.transformers_}) ^^^^^^^^^^^^^^^^^^ AttributeError: 'ColumnTransformer' object has no attribute 'transformers_'. Did you mean: 'transformers'?

import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.preprocessing import MinMaxScaler from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn.metrics import classification_report from sklearn.metrics import confusion_matrix

这段代码是在 Python 中导入了一些常用的机器学习库和模块，包括 pandas、numpy、matplotlib、sklearn 等。其中： - pandas 是 Python 中常用的数据分析库，可以用来读取和处理数据； - numpy 是 Python 中常用的...

import numpy as np import matplotlib.pyplot as plt import pandas as pd import seaborn as sns from sklearn.datasets import load_boston from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn import metrics from sklearn import preprocessing

你给出的是 Python 的代码，我看到你正在使用一些流行的 Python 库，如 NumPy、matplotlib、pandas 和 seaborn，以及一些机器学习相关的库，如 scikit-learn。这些库都是非常有用的，能够帮助你进行数据分析、可视化...

import numpy as np from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import accuracy_score random_state=42 n_samples=1000 n_features=20 X=np.random.rand(n_samples,n_features) y=np.random.randint()按这个来

from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import accuracy_score # 固定随机种子确保可复现性 random_state = 42 np....

import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import load_iris from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split，这是什么意思

具体来说，该程序使用load_iris函数加载鸢尾花数据集，然后使用train_test_split函数将数据集随机划分为训练集和测试集。接着，使用LogisticRegression函数创建一个逻辑回归分类器，并使用训练集进行拟合。最后，...

写完这些代码后： import pandas as pd import numpy as np from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix,classification_report from sklearn.metrics import roc_curve import matplotlib.pyplot as plt from sklearn.linear_model import logisticRegression 出现了下面的错误 partially initialized module 'sklearn' has no attribute 'version' (most likely due to a circular import)请问这个错误怎么修改

这个错误通常是由于环境问题导致的。尝试升级 scikit-learn 库到最新版本，可以使用以下命令来升级： ...另外，确保在代码文件中没有命名为 "sklearn.py" 的文件，这可能会导致命名冲突并导致该错误。

import pandas as pd import numpy as np import winreg import sklearn from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split from sklearn.linear_model import Ridge###导入岭回归算法 from sklearn.metrics import r2_score import winreg

你已经成功导入了所需的库：pandas、numpy、winreg、sklearn以及sklearn中的LinearRegression、train_test_split、Ridge和r2_score。现在你可以使用这些库中提供的功能来进行数据处理、建模和评估等任务。如果你...

import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split啥意思

具体来说，该程序使用load_iris函数加载鸢尾花数据集，并使用train_test_split函数将数据集随机划分为训练集和测试集，以便进行模型训练和评估。另外，还使用了numpy和matplotlib.pyplot库进行数据处理和可视化。...

from sklearn.datasets import load_boston import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.linear_model import LassoCV import seaborn as sns from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split

其中，load_boston函数用于加载波士顿房价数据集，LassoCV是一种线性回归模型，可以进行自动特征选择和正则化，StandardScaler用于对数据进行标准化处理，train_test_split用于将数据集划分为训练集和测试集。

import pandas as pd import numpy as np import matplotlib.pyplot as plt from pylab import * import seaborn as sns import os from scipy import stats from sklearn import model_selection, preprocessing, naive_bayes, metrics, svm from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.linear_model import LogisticRegression from sklearn.neighbors import KNeighborsClassifier from sklearn import ensemble, tree # 忽略警告提示

这段代码的作用是导入所需的Python库和模块，并忽略警告提示。其中包括： - pandas：用于数据处理和分析的Python库。 - numpy：用于数值计算的Python库。 - matplotlib：用于绘制数据可视化图表的Python库。 - ...

import pandas as pd import os from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score # 计算准确性 import numpy as np from sklearn.tree import plot_tree import matplotlib.pyplot as plt # 画图 from sklearn.metrics import confusion_matrix # 绘制混淆矩阵时使用 from sklearn.metrics import roc_curve, auc # 绘制ROC曲线时使用 from sklearn.preprocessing import label_binarize # 使用sklearn中的LabelBinarizer可以将多分类标签转化为二分类标签 from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import LabelBinarizer

- 使用sklearn中的train_test_split函数将数据集分为训练集和测试集； - 使用RandomForestClassifier（随机森林分类器）进行分类任务； - 使用accuracy_score函数计算模型的准确度； - 导入numpy库； - 使用plot_...

import pandas as pd from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score import numpy as np data = pd.read_csv('iris.csv', header=None, names=['sepald

3. 使用train_test_split函数将数据集划分为训练集和测试集，其中测试集占总数据集的50%。 4. 创建DecisionTreeClassifier类的实例，并将其存储在clf变量中。 5. 使用fit方法在训练集上训练决策树模型。 6. 使用...

import pandas as pd import openpyxl # import matplotlib.pyplot as plt import numpy as np from sklearn.ensemble import AdaBoostClassifier from sklearn.model_selection import train_test_split # 打开Excel文件 wb = openpyxl.load_workbook('./处理过的训练集/987027.xlsx')

其中，pandas库用于数据的处理和分析，openpyxl库用于读写Excel文件，numpy库用于数值计算，sklearn库用于机器学习算法的实现。对于打开Excel文件的操作，需要指定文件路径和文件名，本例中文件路径为'./处理过的...

import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn import metrics import matplotlib.pyplot as plt

- from sklearn.model_selection import train_test_split 导入了 scikit-learn 库中的 train_test_split 函数，用于将数据集拆分为训练集和测试集。 - from sklearn.linear_model import LogisticRegression ...

解释如下代码：import numpy as np import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression from sklearn.datasets import make_regression from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.metrics import mean_squared_error

这段代码是导入了一些 Python 的第三方库，其作用如下： 1. numpy：Python 数组计算库，提供了高效的数组运算功能。 2. matplotlib.pyplot：Python 绘图库，用于绘制图形和数据可视化。 3. sklearn.linear_...

运行下面代码需要安装哪些包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

运行上述代码需要安装以下Python包： - scikit-learn：提供了机器学习算法和工具，包括模型选择、特征提取、预处理等。可以使用pip install scikit-learn进行安装。 - pandas：提供了高性能、易于使用的数据结构...

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

from sklearn.model_selection import train_test_split, StratifiedShuffleSplit, StratifiedKFold, GridSearchCV, LeaveOneOut, cross_val_predict, KFold from sklearn.decomposition import PCA from sklearn....

在Python中想运行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怎么操作

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 ...

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import numpy as np import matplotlib.pyplot as plt import pandas as pd import seaborn as sns from sklearn.datasets import load_boston from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn import metrics from sklearn import preprocessing

import numpy as np from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import accuracy_score random_state=42 n_samples=1000 n_features=20 X=np.random.rand(n_samples,n_features) y=np.random.randint()按这个来

import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import load_iris from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split，这是什么意思

import pandas as pd import numpy as np import winreg import sklearn from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split from sklearn.linear_model import Ridge###导入岭回归算法 from sklearn.metrics import r2_score import winreg

import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split啥意思

from sklearn.datasets import load_boston import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.linear_model import LassoCV import seaborn as sns from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split

import pandas as pd from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score import numpy as np data = pd.read_csv('iris.csv', header=None, names=['sepald

import pandas as pd import openpyxl # import matplotlib.pyplot as plt import numpy as np from sklearn.ensemble import AdaBoostClassifier from sklearn.model_selection import train_test_split # 打开Excel文件 wb = openpyxl.load_workbook('./处理过的训练集/987027.xlsx')

import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn import metrics import matplotlib.pyplot as plt

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