Summary
In this chapter, we explored three common methods for scaling numerical features: min-max scaling, z-score scaling, and robust scaling. Min-max scaling transforms data to a specific range, making it suitable for algorithms sensitive to feature magnitudes. Z-score scaling standardizes data to zero mean and unit variance, providing a standardized distribution. Robust scaling, robust to outliers, employs the median and interquartile range, making it suitable for datasets with skewed distributions or outliers. We also went through different considerations to keep in mind while deciding on the best approach for your use case.
Moving forward, we’ll shift our focus to handling categorical features in the next chapter.
