泰坦尼克号数据集Corr PointBiserial 与 LogisticReg结果不同

如何解决泰坦尼克号数据集Corr PointBiserial 与 LogisticReg结果不同

我想检查logistic reg之间的相关结果。和 pointBiserial 为续。列，但结果不同。

比如

## CORRELATION CHECK  NUMBERIC INPUTS VS BINARY (0/1) CATEGORICAL OUTPUT  (pointbiserialr)
#The point-biserial correlation correlates a binary variable Y and a continuous variable X.
from scipy.stats import *
column_names = ["col_name","correlation","p_value"]
corr_numeric = pd.DataFrame(columns = column_names) # create dataframe for holding corr and p_values for each column

list1 = df_train['Survived']

for col in df_train[con_input_Col_list].columns:
    list2=df_train[col]
    # Apply the PointbiserialrResult() 
    corr_numeric.loc[-1] = [col,stats.pointbiserialr(list1,list2)[0],list2)[1]] 
    corr_numeric.index = corr_numeric.index + 1 

#print the columns which reject null hypo
print(corr_numeric) 
print("-------------------------")
print(corr_numeric.query("correlation>0.25 or correlation<-0.25")) 

RESULT:

  col_name  correlation       p_value
0     fare     0.257307  6.120189e-15


    FOR LOG.REG. Corr.---------------------------------
    
    ## CORRELATION CHECK  NUMBERIC INPUTS VS BINARY (0/1) CATEGORICAL OUTPUT (Log Reg.)
    
    from sklearn.linear_model import LogisticRegression
    import statsmodels.api as sm
    from sklearn.model_selection import train_test_split
    
    # Defining X and y
    y=df_train['Survived']
    X=df_train[con_input_Col_list]
    # Adding the column of ones so it can provide intercept.
    X=sm.add_constant(X)
    #Splitting the dataset
    X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=0)
    model=sm.Logit(y_train,X_train,random_state=0)
    result=model.fit()
    #print(result.summary())
    
    # Fetching the statistics
    stat_df=pd.DataFrame({'coefficients':result.params,'p-value': result.pvalues,'odds_ratio': np.exp(result.params)})
    display(stat_df)
    # Condition for significant parameters
    significant_params=stat_df[stat_df['p-value']<=0.05].index
    significant_params= significant_params.drop('const')
    significant_params

Index(['Age','SibSp','fare'],dtype='object')

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虽然 pointBiserial 仅显示具有良好相关性的票价列。分数，Log.Reg。 corr 说 'Age'、'SibSp'、'fare' 都可以。为什么？