StackedML
Practice
Labs
Questions
Models
Pricing
Sign in
Questions
/
ML Fundamentals
/
Core Concepts
/
Feature engineering basics
← Previous
Next →
133.
Consistent Feature Transformation
easy
Why is it important to apply the same feature transformations to training and test data?
A
To guarantee that both splits have identical statistical distributions after transformation
B
To prevent the test set from having a different number of features than the training set
C
To satisfy the i.i.d. assumption required for all supervised learning algorithms
D
To ensure the test data lives in the same feature space the model was trained on
Sign in to verify your answer
← Back to Questions