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ML Pythonprogramming~10 mins

Feature scaling (StandardScaler, MinMaxScaler) in ML Python - Interactive Code Practice

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Practice - 5 Tasks
Answer the questions below
1fill in blank
easy

Complete the code to import the StandardScaler from scikit-learn.

ML Python
from sklearn.preprocessing import [1]
Drag options to blanks, or click blank then click option'
ARobustScaler
BMinMaxScaler
CNormalizer
DStandardScaler
Attempts:
3 left
2fill in blank
medium

Complete the code to create a MinMaxScaler instance.

ML Python
scaler = [1]()
Drag options to blanks, or click blank then click option'
AMinMaxScaler
BStandardScaler
CNormalizer
DLabelEncoder
Attempts:
3 left
3fill in blank
hard

Fix the error in the code to fit the scaler to the data array X.

ML Python
scaler.[1](X)
Drag options to blanks, or click blank then click option'
Atransform
Bfit_transform
Cfit
Dscale
Attempts:
3 left
4fill in blank
hard

Fill both blanks to scale the data X using fit and transform methods.

ML Python
scaler.[1](X)
X_scaled = scaler.[2](X)
Drag options to blanks, or click blank then click option'
Afit
Btransform
Cfit_transform
Dinverse_transform
Attempts:
3 left
5fill in blank
hard

Fill all three blanks to create a StandardScaler, fit it on training data, and transform test data.

ML Python
scaler = [1]()
scaler.[2](X_train)
X_test_scaled = scaler.[3](X_test)
Drag options to blanks, or click blank then click option'
AMinMaxScaler
Bfit
Ctransform
DStandardScaler
Attempts:
3 left