Explore the significance of scaling numeric variables in training data for AI models. Learn how feature engineering impacts model accuracy and performance!
Table of Contents
Question
Ensuring that the numeric variables in training data are on a similar scale is an example of __________.
A. data ingestion
B. feature engineering
C. feature selection
D. model training
Answer
B. feature engineering
Explanation
Feature engineering involves preparing and modifying input variables to improve model performance. Scaling numeric variables to a similar range helps prevent biases in models that might favor larger magnitude features, ensuring fair consideration of all features during model training.
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