What is data normalization in machine learning?

What is data normalization in machine learning?

Data normalization is the process of scaling individual data points to a common scale, often between 0 and 1.

Why is normalization important for machine learning models?

Normalization helps improve the convergence speed of algorithms and ensures that features contribute equally to the distance calculations.

Can you explain different normalization techniques?

Sure! Common techniques include Min-Max scaling, Z-score normalization, and Robust scaling.

When should I use normalization in my data preprocessing?

Normalization is particularly useful when your data features have different units or scales, especially for algorithms like KNN or neural networks.