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
时间: 2023-10-26 14:04:45 浏览: 65
您的代码看起来像是在使用Python中的一些流行的机器学习库,如Pandas、Scikit-learn和Numpy。通过这些库,您可以进行数据处理、训练和评估机器学习模型。
具体来说,您的代码中使用了以下功能:
- 导入了pandas、os、sklearn等库;
- 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_tree函数绘制决策树;
- 使用confusion_matrix函数绘制混淆矩阵;
- 使用roc_curve和auc函数绘制ROC曲线;
- 使用LabelEncoder和LabelBinarizer函数将标签进行编码。
总的来说,这些功能可以帮助您构建和评估机器学习模型。
相关问题
# 导入必要的库 import pandas as pd from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score # 加载数据集并准备自变量和因变量 df= pd.read_csv(r"D:\数学建模\重航数学建模校赛\最终类别.csv", encoding='gbk') X = df["组别"] y = df['网民数目'] # 因变量为 y # 将数据集划分为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) # 创建随机森林模型并在训练集上拟合模型 rf = RandomForestClassifier(n_estimators=100, max_depth=5) rf.fit(X_train, y_train) # 使用测试集来评估模型的性能 y_pred = rf.predict(X_test) accuracy = accuracy_score(y_test, y_pred) print('Accuracy:', accuracy)(代码改进)
# 导入必要的库
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# 加载数据集并准备自变量和因变量
df = pd.read_csv(r"D:\数学建模\重航数学建模校赛\最终类别.csv", encoding='gbk')
X = df[["组别"]] # 自变量为 组别
y = df['网民数目'] # 因变量为 网民数目
# 将数据集划分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# 创建随机森林模型并在训练集上拟合模型
rf = RandomForestClassifier(n_estimators=100, max_depth=5)
rf.fit(X_train, y_train)
# 使用测试集来评估模型的性能
y_pred = rf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)
from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score from lime.lime_tabular import LimeTabularExplainer import numpy as np import pandas as pd # 准备数据 data = load_breast_cancer() # df=pd.DataFrame(data.data,columns=data.feature_names) # df['target']=data.target # print(df.head()) X = data.data y = data.target # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) # 训练模型 rfc = RandomForestClassifier(n_estimators=100, random_state=42) rfc.fit(X_train, y_train) # 预测结果 y_pred = rfc.predict(X_test) accuracy = accuracy_score(y_test, y_pred) print(f"Accuracy:{accuracy:.3f}") # 解释模型结果 def explain_sample(x, model, feature_names): explainer = LimeTabularExplainer(X_train, feature_names=feature_names, class_names=data.target_names) exp = explainer.explain_instance(x, model.predict_proba, num_features=len(feature_names)) return exp # 随机选择一个测试样本并解释 idx = np.random.randint(len(X_test)) x=X_test[idx] exp=explain_sample(x,rfc,data.feature_names) fig=exp.as_pyplot_figure() print(f"Sample index:{idx}") fig.show()优化一下这段代码,让可视化图片不要一闪而过
可以使用`input()`函数让程序等待用户输入后再继续执行,这样就可以让可视化图片在用户输入后再显示。代码如下:
```python
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from lime.lime_tabular import LimeTabularExplainer
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# 准备数据
data = load_breast_cancer()
X = data.data
y = data.target
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# 训练模型
rfc = RandomForestClassifier(n_estimators=100, random_state=42)
rfc.fit(X_train, y_train)
# 预测结果
y_pred = rfc.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy:{accuracy:.3f}")
# 解释模型结果
def explain_sample(x, model, feature_names):
explainer = LimeTabularExplainer(X_train, feature_names=feature_names, class_names=data.target_names)
exp = explainer.explain_instance(x, model.predict_proba, num_features=len(feature_names))
return exp
# 随机选择一个测试样本并解释
idx = np.random.randint(len(X_test))
x=X_test[idx]
exp=explain_sample(x,rfc,data.feature_names)
fig=exp.as_pyplot_figure()
print(f"Sample index:{idx}")
fig.show()
input("Press Enter to continue...")
```
这样,当程序运行到`fig.show()`时,会显示可视化图片,并等待用户输入后才会继续执行下去。用户可以在图片显示的状态下仔细观察,直到输入回车键后程序才会结束。