过采样和上采样之间以及 SMOTE 和 over_sampling.SMOTE 之间的区别?
Difference between over sampling and upsampling and between SMOTE and over_sampling.SMOTE?
这个问题有点偏执,因为 google 搜索结果被音频和傅立叶变换等混杂了。
具体针对python,在数值数据方面,少数class的oversampling和upsampling有区别吗?
我正在使用 imblearn oversample/upsample 少数 class。我目前正在使用
from imblearn.over_sampling import SMOTE
sm = SMOTE(random_state=12, ratio = 1.0)
x_train_res, y_train_res = sm.fit_sample(X_train, y_train)
但最近,我遇到了
sm = over_sampling.SMOTE(random_state=12, ratio = 1.0)
x_train_res, y_train_res = sm.fit_sample(X_train, y_train)
有什么区别?
from imblearn.over_sampling import SMOTE
sm = SMOTE(random_state=12, ratio = 1.0)
和
import imblearn.over_sampling
sm = over_sampling.SMOTE(random_state=12, ratio = 1.0)
完全相同。唯一的区别是您如何在代码中访问 SMOTE 函数。
这个问题有点偏执,因为 google 搜索结果被音频和傅立叶变换等混杂了。
具体针对python,在数值数据方面,少数class的oversampling和upsampling有区别吗?
我正在使用 imblearn oversample/upsample 少数 class。我目前正在使用
from imblearn.over_sampling import SMOTE sm = SMOTE(random_state=12, ratio = 1.0) x_train_res, y_train_res = sm.fit_sample(X_train, y_train)
但最近,我遇到了
sm = over_sampling.SMOTE(random_state=12, ratio = 1.0) x_train_res, y_train_res = sm.fit_sample(X_train, y_train)
有什么区别?
from imblearn.over_sampling import SMOTE
sm = SMOTE(random_state=12, ratio = 1.0)
和
import imblearn.over_sampling
sm = over_sampling.SMOTE(random_state=12, ratio = 1.0)
完全相同。唯一的区别是您如何在代码中访问 SMOTE 函数。