[sklearn][standardscaler] 我可以反转模型输出的standardscaler吗?
问题描述：
我有一些结构如下的数据，试图从特征中预测 t.
I have some data structured as below, trying to predict t from the features.
train_df
t: time to predict
f1: feature1
f2: feature2
f3:......
t 是否可以用 StandardScaler 进行缩放，所以我改为预测 t'，然后对 StandardScaler 求逆以获取实时?
Can t be scaled with StandardScaler, so I instead predict t' and then inverse the StandardScaler to get back the real time?
例如:
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(train_df['t'])
train_df['t']= scaler.transform(train_df['t'])
运行回归模型，
检查分数，
！！用实时值检查预测的 t'(逆 StandardScaler)
!! check predicted t' with real time value(inverse StandardScaler) <- possible?