KNN决策树探究泰坦尼克号幸存者问题

import pandas as pd
from sklearn.tree import DecisionTreeClassifier, export_graphviz
from sklearn.metrics import classification_report
import graphviz   #决策树可视化

data = pd.read_csv(r"titanic_data.csv")
data.drop("PassengerId",axis = 1,inplace = True)  #删除id这一列

data

891 rows × 4 columns

data.loc[data["Sex"] == "male","Sex"] = 1
data.loc[data["Sex"] == "female","Sex"] = 0

data

891 rows × 4 columns

data.fillna(data["Age"].mean(),inplace = True)  #用均值来填充缺失值

data

891 rows × 4 columns

Dtc = DecisionTreeClassifier(max_depth = 5,random_state =8)  #构建决策树
Dtc.fit(data.iloc[:,1:],data["Survived"])    #模型训练
pre = Dtc.predict(data.iloc[:,1:])  #模型预测

print(classification_report(pre,data["Survived"]))   #混淆矩阵

              precision    recall  f1-score   support

           0       0.88      0.84      0.86       573
           1       0.73      0.79      0.76       318

    accuracy                           0.82       891
   macro avg       0.81      0.82      0.81       891
weighted avg       0.83      0.82      0.82       891

pre == data["Survived"]   #比较模型预测值与实际值是否一致

0       True
1       True
2       True
3       True
4       True
       ...  
886     True
887     True
888    False
889    False
890     True
Name: Survived, Length: 891, dtype: bool

可视化

dot_data = export_graphviz(Dtc,feature_names = ["Pclass","Sex","Age"],class_names="Survive")

graph  = graphviz.Source(dot_data)
graph