Scaling Features Crucial In Machine Learning Models
Feature scaling should be done after splitting dataset into training & test sets to prevent data leakage & ensure accurate model evaluation. Split first, then scale using techniques like Min-Max Scaling or Standardization (Z-score Normalization).
When working with machine learning models, data preprocessing plays a critical role in ensuring accuracy and effectiveness. One essential preprocessing step is feature scaling. However, an often-overlooked aspect is the order in which these steps are performed. In this article, we will explore why feature scaling should always be done after splitting your dataset into training and test sets. We’ll cover its benefits, the pitfalls of scaling before splitting, and best practices for implementing this process effectively. Understanding Feature Scaling in Machine Learning Feature scalin...