Normalization and Standardization
Feature scaling, normalization, and standardization are essential preprocessing steps that help ensure that machine learning models can effectively learn from data. These techniques address issues related to numerical stability, algorithm convergence, model performance, and more, ultimately contributing to better, more reliable results in data analysis and machine learning tasks.
In this chapter, we will dive deep into the following topics:
- Scaling features to a range
- Z-score scaling
- Robust scaling
