Atlas · GenAI 2026
Feature Scaling
Selecting and fitting feature-scale transformations such as standardization, min–max or robust scaling, with attention to outliers, sparse inputs and train–test separation.
conceptFeature EngineeringAI consensus: 0/3
Prerequisites
Distributions, scale and outliers inform the transformation choice.
Recommended reference
scikit-learn: Importance of Feature Scaling — https://scikit-learn.org/stable/auto_examples/preprocessing/plot_scaling_importance.html; scikit-learn: Preprocessing data — https://scikit-learn.org/stable/modules/preprocessing.html
Reviewed sources
Primary and first-party material reviewed for this editorial summary. These citations are separate from the AI consensus score above.
- scikit-learn: Importance of Feature Scaling
Effect of feature scale on distance-based models and PCA.
- scikit-learn: Preprocessing data
Feature transformations, scaling and categorical encoding.
- scikit-learn: StandardScaler
Training-set statistics, sparse inputs and centering/scaling behavior.
Notes from AI deep research
Related skills
- → is part of: Feature Engineering