What is the primary objective of feature scaling in a dataset?
- Improve model interpretability
- Enhance visualization
- Ensure all features have equal importance
- Make different feature scales compatible
The primary objective of feature scaling is to make features with different scales or units compatible so that machine learning algorithms, particularly those based on distance metrics, are not biased towards features with larger scales. This ensures that each feature contributes equally to the model's performance. Improving interpretability and visualization may be secondary benefits of feature scaling, but the main goal is compatibility.
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