使用logisticRegression模型跑出來得結果會不相同

# 空值補 -1, 做羅吉斯迴歸
df_m1 = df.fillna(-1)
train_X = df_m1[:train_num]
estimator = LogisticRegression(solver='lbfgs', multi_class='auto')
cross1 = cross_val_score(estimator, train_X, train_Y, cv=5).mean()
print( 'The accuracy fills with -1 when na found: ', cross1 )


df_m2 = df.fillna(0)
train_X2 = df_m2[:train_num]
#estimator = LogisticRegression(solver='lbfgs', multi_class='auto')
cross2 = cross_val_score(estimator, train_X2, train_Y, cv=5).mean()
print( 'The accuracy fills with 0 when na found: ', cross2 )

df_m3 = df.fillna(df.mean())
train_X3 = df_m3[:train_num]
#estimator = LogisticRegression(solver='lbfgs', multi_class='auto')
cross3 = cross_val_score(estimator, train_X3, train_Y, cv=5).mean()
print( 'The accuracy fills with mean when na found: ', cross3 )

'''
The accuracy fills with -1 when na found:  0.6982644788418415
The accuracy fills with 0 when na found:  0.6993817972775958
The accuracy fills with mean when na found:  0.6959413955734954
效果最好當fillna = 0
'''

# 空值補 -1, 做羅吉斯迴歸
df_m1 = df.fillna(-1)
train_X = df_m1[:train_num]
estimator = LogisticRegression(solver='lbfgs', multi_class='auto')
cross1 = cross_val_score(estimator, train_X, train_Y, cv=5).mean()
print( 'The accuracy fills with -1 when na found: ', cross1 )


df_m2 = df.fillna(0)
train_X2 = df_m2[:train_num]
#estimator = LogisticRegression(solver='lbfgs', multi_class='auto')
cross2 = cross_val_score(estimator, train_X2, train_Y, cv=5).mean()
print( 'The accuracy fills with 0 when na found: ', cross2 )

df_m3 = df.fillna(df.mean())
train_X3 = df_m3[:train_num]
#estimator = LogisticRegression(solver='lbfgs', multi_class='auto')
cross3 = cross_val_score(estimator, train_X3, train_Y, cv=5).mean()
print( 'The accuracy fills with mean when na found: ', cross3 )

'''
The accuracy fills with -1 when na found:  0.6982644788418415
The accuracy fills with 0 when na found:  0.6993817972775958
The accuracy fills with mean when na found:  0.6959413955734954
效果最好當fillna = 0
'''

使用logisticRegression模型跑出來得結果會不相同

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