#将matplotlib的图表直接嵌入到Notebook之中 %matplotlib inline from sklearn.ensemble import RandomForestClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.datasets import load_wine from sklearn.model_selection import train_test_split #训练测试划分 Xtain,Xtest,Ytrain,Ytest=train_test_split(wine.data,wine.target,test_size=0.3) #确定随机的样本，使数值不再变化 clf=DecisionTreeClassifier(random_state=0) rfc=RandomForestClassifier(random_state=0) clf=clf.fit(Xtrain,Ytrain) rfc=rfc.fit(Xtrain,Ytrain) score_c=clf.score(Xtest,Ytest) score_r=rfc.score(Xtest,Ytest) #.format一个连接,一棵树和随机森林返回的结果，一般随机森林的准确性会更高一点 print("single Tree:{}".format(score_c),"random Forest:{}".format(score_r))

你的代码片段中使用了 `%matplotlib inline` 这个魔术命令，该命令用于在 Jupyter Notebook 中将 matplotlib 图表直接嵌入到 Notebook 中。但是，在你的代码中，我没有看到导入 matplotlib 的语句，也没有看到你使用 matplotlib 来创建图表并显示。

在你的代码中，你只是使用了 `print()` 函数来打印单一决策树和随机森林分类器的准确性。如果你想直接在 Notebook 中显示图表，你需要使用 matplotlib 的绘图函数（例如 `plt.plot()`、`plt.scatter()` 等）来创建图表，并使用 `plt.show()` 函数来显示图表。

以下是一个示例，显示如何在 Notebook 中创建一个简单的柱状图：

%matplotlib inline
import matplotlib.pyplot as plt

# 假设你已经计算了两个模型的分数
score_c = 0.85
score_r = 0.92

# 创建柱状图
plt.bar(['Single Tree', 'Random Forest'], [score_c, score_r])
plt.xlabel('Model')
plt.ylabel('Accuracy')
plt.title('Model Comparison')

# 显示图表
plt.show()

你可以根据你的需求和数据来调整图表的类型和样式。希望这能帮助到你！如果你有更多问题，请随时提问。

相关问题

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。

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。