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

python scikit-learn scale
hyon picture hyon · Jun 14, 2017 · Viewed 16.3k times · Source

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

Arya McCarthy picture Arya McCarthy · Jun 14, 2017

Yeah, and it's conveniently called inverse_transform.

The documentation provides examples of its use.

Related questions

scikit-learn: how to scale back the 'y' predicted result

I'm trying to learn scikit-learn and Machine Learning by using the Boston Housing Data Set. # I splitted the initial dataset ('housing_X' and 'housing_y') from sklearn.cross_validation import train_test_split X_train, X_test, y_train, y_…

Read More
Apply StandardScaler to parts of a data set

I want to use sklearn's StandardScaler. Is it possible to apply it to some feature columns but not others? For instance, say my data is: data = pd.DataFrame({'Name' : [3, 4,6], 'Age' : [18, 92,98], 'Weight' : [68, 59,49]}) Age Name Weight 0 18 3 68 1 92 4 59 2 98 6 49 col_names = ['Name', 'Age', 'Weight'] features = …

Read More
How to normalize an array in NumPy?

I would like to have the norm of one NumPy array. More specifically, I am looking for an equivalent version of this function def normalize(v): norm = np.linalg.norm(v) if norm == 0: return v return v / norm Is there …

Read More