What is feature scaling in machine learning?

What is feature scaling in machine learning?

Feature scaling is the process of normalizing or standardizing the range of independent variables in your data.

Why is feature scaling important?

It's important because it helps algorithms converge faster and improves the performance of models, especially those sensitive to the scale of data.

What are the common methods of feature scaling?

Common methods include Min-Max scaling, Standardization (Z-score normalization), and Robust scaling.

When should I use Min-Max scaling over Standardization?

Use Min-Max scaling when your data is uniformly distributed and you want to preserve the relationships, while Standardization is better for normally distributed data.