1.导入相应包

import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np

2.准备数据集（需要数据集可以评论）

#不同路径下选择不同路径
data = pd.read_csv(r"D:\download\sklearnjqxx_jb51\【机器学习】菜菜的sklearn课堂(1-12全课)\01 决策树课件数据源码\data.csv", index_col=0)
data.head()

3.查看数据信息

data.info()

查看数据信息如下：

<class 'pandas.core.frame.DataFrame'>
Int64Index: 891 entries, 1 to 891
Data columns (total 11 columns):
Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   Survived  891 non-null    int64  
 1   Pclass    891 non-null    int64  
 2   Name      891 non-null    object 
 3   Sex       891 non-null    object 
 4   Age       714 non-null    float64
 5   SibSp     891 non-null    int64  
 6   Parch     891 non-null    int64  
 7   Ticket    891 non-null    object 
 8   Fare      891 non-null    float64
 9   Cabin     204 non-null    object 
 10  Embarked  889 non-null    object 
dtypes: float64(2), int64(4), object(5)
memory usage: 83.5+ KB

3.数据预处理

#删除缺失值过多的列，和观察判断来说和预测的y没有关系的列
data.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
data.head()
#处理缺失值，对缺失值较多的列进行填补，有一些特征只确实一两个值，可以采取直接删除记录的方法
data['Age'] = data['Age'].fillna(data['Age'].mean())
data = data.dropna()
#将分类变量转换为数值型
data['Sex'] = data['Sex'].map({'male' : 1, 'female' : 0})
#也可采用下面这条语句进行转换
#data['sex'] = (data['sex']=='male').astype('int')
#将三分类变量转换为数值型变量,也可直接采用map函数或apply
labels = data["Embarked"].unique().tolist()
data["Embarked"] = data["Embarked"].apply(lambda x: labels.index(x))
data.head()

4.提取标签和特征矩阵，分测试集和训练集

 X = data.iloc[:, data.columns != "Survived"]
 y = data.iloc[:, data.columns == "Survived"]
 #利用内置函数划分训练集测试集
 Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, y, test_size=0.3)

5.导入模型，粗略跑一下查看结果

#clf精度计算
clf = DecisionTreeClassifier(random_state=25)
clf.fit(Xtrain, Ytrain)
score = clf.score(Xtest, Ytest)
score

#十折交叉验证
score = cross_val_score(clf, X, y, cv=10).mean()
score

6.在不同max_depth下观察模型的拟合状况

tr = []
te = []
for i in range(10):
    clf = DecisionTreeClassifier(random_state=25
                                ,max_depth=i+1
                                ,criterion='entropy')
    clf.fit(Xtrain, Ytrain)
    #记录两种结果
    score_tr = clf.score(Xtest, Ytest)
    score_te = cross_val_score(clf, X, y, cv=10).mean()
    tr.append(score_tr)
    te.append(score_te)

7.结果可视化

plt.figure()
plt.plot(range(1, 11), tr, color='red', label='train')
plt.plot(range(1, 11), te, color='blue', label='test')
plt.legend()
plt.xticks(range(1, 11))
plt.show()

结果图如下

我们从上图发现我们的结果有一些欠拟合，我们下一步采用进一步网格搜索对参数进行优化。

8.用网格搜索调整参数

gini_thresholds = np.linspace(0,0.5,20)
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)]
             }
clf = DecisionTreeClassifier(random_state=25)
GS = GridSearchCV(clf, parameters, cv=10)
GS.fit(Xtrain,Ytrain)
GS.best_params_
GS.best_score_

输出的最佳参数为

{'criterion': 'gini',
'max_depth': 9,
'min_impurity_decrease': 0.0,
'min_samples_leaf': 6,
'splitter': 'best'}

最佳正确率为0.8458013312852021。