Interpretation of ML: A case of global interpretation/local interpretation of EBC model interpretability based on titanic titanic rescued binary prediction data set using interpret

ML之interpret：基于titanicTitanic rescued binary prediction dataset utilizationinterpret实现EBCA global explanation of model interpretability/Partial explanation of the case

目录

基于titanicTitanic rescued binary prediction dataset utilizationinterpret实现EBCA global explanation of model interpretability/Partial explanation of the case

# 1、定义数据集

# 2、数据预处理

# 2.1、类别特征编码

# 2.2、Features are filtered and filled with null values

# 3、模型建立与训练

# 3.1、数据集切分

# 3.2、建立EBC模型

# 3.3、基于EBCModels enable model interpretability

# (1)、overall interpretability

# (2)、local interpretability

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ML之interpret：基于titanicTitanic rescued binary prediction dataset utilizationinterpret实现EBCA global explanation of model interpretability/Partial explanation of the case
ML之interpret：基于titanicTitanic rescued binary prediction dataset utilizationinterpret实现EBCA global explanation of model interpretability/Partial explanation case implementation

基于titanicTitanic rescued binary prediction dataset utilizationinterpret实现EBCA global explanation of model interpretability/Partial explanation of the case

# 1、定义数据集

PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
1	0	3	Braund, Mr. Owen Harris	male	22	1	0	A/5 21171	7.25		S
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Thayer)	female	38	1	0	PC 17599	71.2833	C85	C
3	1	3	Heikkinen, Miss. Laina	female	26	0	0	STON/O2. 3101282	7.925		S
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35	1	0	113803	53.1	C123	S
5	0	3	Allen, Mr. William Henry	male	35	0	0	373450	8.05		S

# 2、数据预处理

# 2.1、类别特征编码

# 2.2、Features are filtered and filled with null values

   Pclass   Age  SibSp  Parch     Fare  Sex_encoding  Embarked_encoding  \
0       3  22.0      1      0   7.2500             1                  0   
1       1  38.0      1      0  71.2833             0                  1   
2       3  26.0      0      0   7.9250             0                  0   
3       1  35.0      1      0  53.1000             0                  0   
4       3  35.0      0      0   8.0500             1                  0   

   Survived  
0         0  
1         1  
2         1  
3         1  
4         0  

# 3、模型建立与训练

# 3.1、数据集切分

# 3.2、建立EBC模型

# 3.3、基于EBCModels enable model interpretability

# (1)、overall interpretability

Sex_encoding
Pclass
Fare
Age
SibSp
pclass x Sex_encoding
Parch
Embarked_encoding
Age x Fare
Age x SibSp
Parch x Fare
Parch x Sex_encoding
Age x Parch
Pclass x Age
Age x Sex_encoding

 

age,10岁以下,年龄小的,Can increase the chances of survival

fare票价越高,The more you can increase your chances of survival

男性,will reduce the chances of survival

0port i.e.Southampton港口,will reduce the chances of survival

# (2)、local interpretability—Select the test sample data in front5行

the first five samples,Visualize the predicted interpretation for each sample one by one