import numpy as np import pandas as pd from sklearn.feature_selection import mutual_info_classif import warnings warnings.filterwarnings('ignore') data = pd.read_excel('高分修正指标选取.xls') data = data.loc[:, ['killip分级', '收缩压', '入院心率', '年龄', '血肌酐', '全因死亡']] X = data["killip分级"].values.reshape(-1, 1) y = data["全因死亡"].values.reshape(-1, 1) X = np.array(X) y = np.array(y) mutual_info_scores = mutual_info_classif(X, y)[0] print(mutual_info_scores)为何上述代码每次运行结果不一致
时间: 2024-02-05 14:03:05 浏览: 28
上述代码中使用的是互信息法(Mutual Information),它的计算结果可能受样本的随机性影响,因此每次运行得到的结果可能不一致。这是因为互信息法的计算涉及到样本的分布情况和数据量的大小,每次运行时这些因素都可能会有所变化,从而导致结果不一致。
如果想要得到稳定的结果,可以考虑对数据进行多次采样,将采样得到的结果取平均值。另外,也可以尝试使用其他的特征选择方法来得到稳定的结果。
相关问题
import numpy as np import pandas as pd from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score为什么有淡黄色波浪线
这个问题通常是由于VS Code的Python插件(Pylance或其他)无法找到对应的模块而导致的。您可以尝试以下几种方法来解决这个问题:
1. 确保已经安装了需要的模块(NumPy、Pandas、Scikit-learn等)。您可以在终端中使用pip install命令来安装这些模块。
2. 在VS Code中打开您的Python文件。如果您还没有安装Python插件,请安装它。您可以在扩展面板中搜索“Python”,然后选择安装“Python”插件。
3. 在VS Code中打开命令面板。您可以使用快捷键Ctrl+Shift+P(Windows和Linux)或者Cmd+Shift+P(Mac)来打开命令面板。
4. 在命令面板中,输入“Python: Select Interpreter”,并选择您想要使用的Python解释器。
5. 如果您已经安装了需要的模块但仍然无法解决问题,请尝试重新启动VS Code或者重装Python插件。另外,您也可以尝试使用其他Python插件,例如Jupyter或者Anaconda,来解决这个问题。
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:
```bash
pip install scikit-learn pandas matplotlib numpy
```
然后,在你的Python脚本中导入所需的库:
```python
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。