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决策树模型可以处理各种类型的特征（连续型、离散型、类别型等），不需要对特征进行过多的预处理工作，因此非常适合初步探索数据。通过绘制混淆矩阵、ROC曲线和特征变量重要性排序图，可以直观地了解模型的性能表现以及对于预测的重要特征，有助于进一步分析和改进模型。下面开始代码实战。
目录
（1）导入相关模块
（2）导入数据
（3）数据标准化与决策树模型构建
（4）绘制混淆矩阵
（5）计算决策树模型的准确度、精度、召回率、F1度量
（6）参数寻优
（7）绘制ROC曲线
（8）绘制模型的特征变量重要性排序图
（1）导入相关模块
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn import tree
import seaborn as sns
from sklearn.metrics import recall_score
from sklearn.metrics import precision_score
from sklearn.metrics import f1_score
from sklearn.model_selection import train_test_split（2）导入数据
data = pd.read_csv(r'E:\工作\硕士\博客\博客粉丝问题\data.csv',encoding="utf-8")
data = data.fillna(-1)#补全数据
print(data)
#划分训练集和验证集
y=data.iloc[:,-1]
print(y)
X=data.iloc[:,:-1]输出结果：
geologic structure human activity underground water susceptibility 0 0 1 1 1 1 0 1 1 1 2 0 1 1 1 3 0 0 1 1 4 0 1 1 1 .. ... ... ... ... 207 1 1 1 1 208 1 1 0 1 209 1 1 1 1 210 0 1 0 1 211 1 1 1 1[212 rows x 4 columns] 0 1 1 1 2 1 3 1 4 1.. 207 1 208 1 209 1 210 1 211 1 Name: susceptibility, Length: 212, dtype: int64
（3）数据标准化与决策树模型构建
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.25, random_state= 0)
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test= scaler.transform(X_test)
print('训练数据形状：')
print(X_train.shape,y_train.shape)
print('验证测试数据形状：')
(X_test.shape,y_test.shape)
print(y_test)from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier()
classifier = classifier.fit(X_train,y_train)
#prediction
y_pred = classifier.predict(X_test)#Accuracy
from sklearn import metrics
print('Accuracy Score:', metrics.accuracy_score(y_test,y_pred))
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
cm输出结果：
Accuracy Score: 0.6792452830188679array([[17, 6],[11, 19]], dtype=int64)
（4）绘制混淆矩阵
from sklearn import metrics
cm = metrics.confusion_matrix(penguin_tree.predict(X_test), y_test)
plt.figure()
sns.heatmap(cm , annot=True, cmap='Blues')
plt.xlabel('Predicted labels')
plt.ylabel('True labels')
plt.savefig(r'E:\工作\硕士\博客\博客粉丝问题\决策树混淆矩阵.png',bbox_inches ="tight",pad_inches = 1,transparent = True,facecolor ="w",edgecolor ='w',dpi=300,orientation ='landscape')pred=y_pred
pred = classifier.predict(X_test)
pred[:5]（5）计算决策树模型的准确度、精度、召回率、F1度量
from sklearn import metrics
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inlinefrom sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import cohen_kappa_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
print("决策树模型在测试集上的准确度为:", metrics.accuracy_score(pred, y_test))
print("决策树模型在测试集上的精度为:", metrics.average_precision_score(pred, y_test))
print("决策树模型在测试集上的召回率为:", metrics.recall_score(pred, y_test))
print("决策树模型在测试集上的F1度量为:", metrics.f1_score(pred, y_test))输出结果：
树模型在测试集上的准确度为: 0.6792452830188679 树模型在测试集上的精度为: 0.5945408805031447 树模型在测试集上的召回率为: 0.76 树模型在测试集上的F1度量为: 0.6909090909090909
（6）参数寻优
parameters = {'splitter':('best','random'),'criterion':("gini","entropy"),"max_depth":[*range(1,10)],'min_samples_leaf':[*range(1,50,5)],'min_impurity_decrease':[*np.linspace(0,0.5,20)]
}
classifier = DecisionTreeClassifier(random_state=25)
GS = GridSearchCV(classifier, parameters, cv=10)
GS.fit(X_train,y_train)
print(GS.best_params_)
print(GS.best_score_)输出结果：
{'criterion': 'gini', 'max_depth': 3, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'splitter': 'best'}
0.7358333333333333
（7）绘制ROC曲线
import sklearn.metrics as metrics
y_pred_clf = clf.predict_proba(X_test)[:,1]
fpr, tpr, threshold=metrics.roc_curve(y_test,y_pred_clf)
roc_auc = metrics.auc(fpr, tpr)
#plt.plot(fpr, tpr, 'b', label='AUC = %0.2f' % roc_auc)#生成ROC曲线
lw = 2
plt.plot(fpr, tpr, color='darkorange',lw=lw, label='AUC = %0.2f' % roc_auc) ###假正率为横坐标，真正率为纵坐标做曲线
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlim([-0.05, 1.05])
plt.ylim([-0.05, 1.05])
plt.xlabel('1 - Specificity')
plt.ylabel('Sensitivity')
# plt.title('ROCs for Densenet')
plt.legend(loc="lower right")plt.savefig(r'E:\工作\硕士\博客\博客粉丝问题\决策树ROC曲线.png',bbox_inches ="tight",pad_inches = 1,transparent = True,facecolor ="w",edgecolor ='w',dpi=300,orientation ='landscape')输出结果：
（8）绘制模型的特征变量重要性排序图
features_import = pd.DataFrame(X_train.columns, columns=['feature'])
features_import['importance'] =classifier.feature_importances_ # 默认按照gini计算特征重要性
features_import.sort_values('importance', inplace=True)
# 绘图
from matplotlib import pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei'] # 显示中文黑体
# plt.rcParams['axes.unicode_minus'] = False # 负值显示
plt.barh(features_import['feature'], features_import['importance'], height=0.7, color='#008792', edgecolor='#005344') # 更多颜色可参见颜色大全
plt.xlabel('feature importance') # x 轴
plt.ylabel('features') # y轴
plt.xlim(0,1)
plt.title('Feature Importances') # 标题
for a,b in zip( features_import['importance'],features_import['feature']): # 添加数字标签print(a,b)plt.text(a+0.001, b,'%.3f'%float(a)) # a+0.001代表标签位置在柱形图上方0.001处plt.savefig(r'E:\工作\硕士\博客\博客粉丝问题\决策树变量重要性程度.png',bbox_inches ="tight",pad_inches = 1,transparent = True,facecolor ="w",edgecolor ='w',dpi=300,orientation ='landscape')输出结果：
需要数据集的家人们可以去百度网盘（永久有效）获取：
链接：https://pan.baidu.com/s/173deLlgLYUz789M3KHYw-Q?pwd=0ly6
提取码：2138
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