HOTSPOT -
You are performing feature scaling by using the scikit-learn Python library for x.1 x2, and x3 features.
Original and scaled data is shown in the following image.
 
Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Hot Area:
 
















 

Suggested Answer: 
    

Box 1: StandardScaler -
The StandardScaler assumes your data is normally distributed within each feature and will scale them such that the distribution is now centred around 0, with a standard deviation of 1.
Example:
 
All features are now on the same scale relative to one another.
Box 2: Min Max Scaler -
 
Notice that the skewness of the distribution is maintained but the 3 distributions are brought into the same scale so that they overlap.
Box 3: Normalizer -
Reference: alt="Reference Image" />
All features are now on the same scale relative to one another.
Box 2: Min Max Scaler -
 http://benalexkeen.com/feature-scaling-with-scikit-learn/

This question is in DP-100 Exam
For getting Microsoft Azure Data Scientist Associate Certificate


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