In data analysis, especially when predicting outcomes like house prices, we often deal with features that have different scales or units. Let's consider an example with three features:
1. Equal Consideration: Without scaling, features with larger values (like square footage) would dominate the analysis, potentially overshadowing important factors like the number of bedrooms.
2. Algorithm Performance: Many machine learning algorithms perform better or converge faster when features are on a similar scale.
3. Interpretability: Scaled features allow for easier interpretation of their relative importance in the model.