The article explains how numeric feature engineering techniques like normalization and standardization can improve machine learning model performance by addressing skewness and scale differences in fe
atures. It provides practical guidance on when and how to apply these transformations, emphasizing their importance for non-tree-based models and proper implementation to prevent data leakage.
Reasons to Read -- Learn:
when specific machine learning models (like GLMs, KNN, and neural networks) are sensitive to feature distributions and how to address these sensitivities through proper transformation techniques
how to properly implement feature standardization within a recipe blueprint to prevent data leakage between training and test sets while maintaining consistent transformations
practical differences between tree-based and non-tree-based models in handling skewed features, helping you make informed decisions about when feature transformation is necessary
3 min readauthor: A.I Hub
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