from sklearn.linear_model import LogisticRegression from sklearn import metrics import numpy as np import pandas as pd data = pd.read_csv( 'final_data1.csv') Y = data.y X = data.drop('y', axis=1)#归一化 xmin = X.min(axis=0) xmax = X.max(axis=0) X_norm = (X-xmin)/(xmax-xmin) from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X_norm, Y, test_size=0.2, random_state=42) clf = LogisticRegression(random_state=0,multi_class='multinomial') clf.fit(X_norm,Y)y_pred= clf.predict(X_test) y_pred= np.round(y_pred) print(metrics.confusion_matrix(y_test, y_pred)) from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score print('逻辑回归分类模型准确率分值: {0:0.4f}'.format(accuracy_score(y_test, y_pred))) print("逻辑回归分类模型查准率 :", round(precision_score(y_test, y_pred), 4), "\n") print("逻辑回归分类模型召回率 :", round(recall_score(y_test, y_pred), 4), "\n") print("逻辑回归分类模型F1分值:", round(f1_score(y_test, y_pred), 4), "\n") 运行上述代码报错ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].如何解决

这个错误是因为你选择了 `average='binary'`，但是你的目标变量是多分类问题，不适用于二元分类的平均设置。你可以尝试使用其他的平均设置来解决这个问题。

以下是一些可选的平均设置：

- `average=None`：不进行平均，返回每个类别的独立评估指标。
- `average='micro'`：对所有类别的预测结果进行整体评估。
- `average='macro'`：计算每个类别的评估指标，并对其进行简单平均。
- `average='weighted'`：计算每个类别的评估指标，并根据类别样本数量进行加权平均。

你可以根据你的需求选择适合的平均设置。

例如，你可以将以下代码段：

print("逻辑回归分类模型查准率 :", round(precision_score(y_test, y_pred), 4), "\n")

修改为：

print("逻辑回归分类模型查准率 :", round(precision_score(y_test, y_pred, average='weighted'), 4), "\n")

通过设置 `average='weighted'`，可以解决这个错误。你可以在其他评估指标中也使用相同的方法进行修改。希望能帮到你！

相关问题

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

这段代码导入了 pandas、numpy、scikit-learn 和 matplotlib.pyplot 库中的一些常用模块和函数。

- `import pandas as pd` 导入了 pandas 库，并将其重命名为 pd，用于数据处理和分析。
- `import numpy as np` 导入了 numpy 库，并将其重命名为 np，用于数值计算和数组操作。
- `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` 导入了 scikit-learn 库中的逻辑回归模型（LogisticRegression），用于进行分类任务。
- `from sklearn import metrics` 导入了 scikit-learn 库中的 metrics 模块，用于评估模型性能。
- `import matplotlib.pyplot as plt` 导入了 matplotlib 库中的 pyplot 模块，并将其重命名为 plt，用于绘制数据可视化图形。

这段代码的目的是导入所需的库和模块，以便在后续的代码中使用它们进行数据处理、模型训练和评估以及结果可视化等操作。

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

要在Python中运行这些代码，你需要确保已经安装了相应的包。大多数包都可以使用`pip`进行安装。

首先，在命令行中运行以下命令来安装所需的包：

pip install scikit-learn pandas matplotlib numpy

然后，在你的Python脚本中导入所需的库：

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.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_auc_score
import datetime
import multiprocessing as mp
from sklearn.ensemble import StackingClassifier
from sklearn.pipeline import make_pipeline
from sklearn.svm import LinearSVC

import random

请确保在运行这些代码之前，已经安装了所需的Python库。如果遇到任何问题，请确保已正确安装这些库，并且版本与代码兼容。