数据集：
数据集获取，百度网盘：
复制这段内容后打开百度网盘手机App，操作更方便哦链接:
网盘地址
/s/1i9PAcOQ9g15qtMyiCvl3lQ
提取码:6z30
上代码：
import matplotlib.pyplot as pltimport pandas as pdfrom sklearn.tree import DecisionTreeClassifierimport seaborn as snsimport numpy as np# 前三个函数是为了把.xlsx文件转化为.csvdef get_excel_sheet_list(filename):df = pd.read_excel(filename, sheet_name=None,engine='openpyxl')return df.keys()def get_excel_sheet_data(filename, sheetname=None):df = pd.read_excel(filename, sheet_name=sheetname,engine='openpyxl')dataList = df.to_dict(orient='records') # 转换为列表return dataListdef file_trans(Read_filepath):sheet_list = get_excel_sheet_list(Read_filepath)file_list=[]for file in sheet_list:Save_filepath = file + '.csv'file_list.append(Save_filepath)sheetData = pd.DataFrame(get_excel_sheet_data(Read_filepath, file))if sheetData.empty:continuesheetData.to_csv(Save_filepath, index=False, mode='w', encoding='utf-8')return file_list# 补足缺省值和返回训练数据，切片def getDataSet():Read_filepath = "../xiangmu/pythonProject/数据集_25.xlsx"list_file = file_trans(Read_filepath)df = pd.read_csv(list_file[0])# 所用的数据df_usage=df.iloc[:,:]df_usage.columns=['年龄','阶段','地区','文化程度','家庭教养','孩子性格','家庭情感','孩子应对','自我效能','交流情况','上网目的','创伤成长']df_index = df_usage.columns.values# df_usage = df_usage.reindex(np.random.permutation(df_usage.index))# print(df_index)# 数据转化为列表df_list=df_usage.values.tolist()return df_list,df_indexdef splitData(aaaaaaaaaa,bbbbbbbbbb):nrow = len(aaaaaaaaaa)ncol = len(bbbbbbbbbb)nxval=5test_data=[]train_data=[]for ixval in range(nxval):# 每一次循环将count置为零test_index = [a for a in range(nrow) if a % nxval == ixval]train_index = [a for a in range(nrow) if a % nxval != ixval]test_data = [aaaaaaaaaa[r] for r in test_index]train_data = [aaaaaaaaaa[r] for r in train_index]x_train=[]y_train=[]x_test=[]y_test=[]for i in test_data:x_test.append(i[:-1])y_test.append(i[-1])for j in train_data:x_train.append(j[:-1])y_train.append(j[-1])return x_train,y_train,x_test,y_testaa,bb=getDataSet()x_train,y_train,x_test,y_test=splitData(aa,bb)## 定义 决策树模型clf = DecisionTreeClassifier(criterion='entropy',max_depth=10,min_samples_leaf=2,min_samples_split=2)# 在训练集上训练决策树模型clf.fit(x_train, y_train)## 在训练集和测试集上分布利用训练好的模型进行预测train_predict = clf.predict(x_train)test_predict = clf.predict(x_test)from sklearn import metrics## 利用accuracy（准确度）【预测正确的样本数目占总预测样本数目的比例】评估模型效果print('训练准确率:',metrics.accuracy_score(y_train,train_predict))print('测试准确率:',metrics.accuracy_score(y_test,test_predict))## 查看混淆矩阵 (预测值和真实值的各类情况统计矩阵)confusion_matrix_result = metrics.confusion_matrix(test_predict,y_test)print('混淆矩阵:\n',confusion_matrix_result)# 利用热力图对于结果进行可视化plt.figure(figsize=(8, 6))sns.heatmap(confusion_matrix_result, annot=True, cmap='BrBG_r')plt.xlabel('true')plt.ylabel('predict')plt.show()
结果：
准确率达到0.97，已经是很高的数值了
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