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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?
438
StandardScaler standardizes a feature by subtracting the mean and then scaling to unit variance. Unit variance means dividing all the values by the standard deviation.
sklearn. preprocessing . StandardScaler. Standardize features by removing the mean and scaling to unit variance.
StandardScaler() will normalize the features i.e. each column of X, INDIVIDUALLY, so that each column/feature/variable will have μ = 0 and σ = 1 .
Yeah, and it's conveniently called inverse_transform.
The documentation provides examples of its use.
167
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) If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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