[sklearn][standardscaler] can I inverse the standardscaler for the model output?

Question

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:...... 

Can t be scaled with StandardScaler, so I instead predict t' and then inverse the StandardScaler to get back the real time?

For example:

from sklearn.preprocessing import StandardScaler scaler = StandardScaler() scaler.fit(train_df['t']) train_df['t']= scaler.transform(train_df['t']) 

run regression model,

check score,

!! check predicted t' with real time value(inverse StandardScaler) <- possible?

Answer 1

Yeah, and it's conveniently called inverse_transform.

The documentation provides examples of its use.

Answer 2

Here is sample code. You can replace here data with train_df['colunm_name']. Hope it helps.

from sklearn.preprocessing import StandardScaler data = [[1,1], [2,3], [3,2], [1,1]] scaler = StandardScaler() scaler.fit(data) scaled = scaler.transform(data) print(scaled)  # for inverse transformation inversed = scaler.inverse_transform(scaled) print(inversed)