How does normalization affect model performance?

What is normalization in the context of machine learning?

Normalization is the process of scaling input features to a similar range, often between 0 and 1, to improve model performance.

How does normalization improve the performance of a model?

It helps in speeding up the convergence of gradient descent, reduces the risk of getting stuck in local minima, and ensures that all features contribute equally.

Are there different methods of normalization?

Yes, common methods include Min-Max scaling, Z-score normalization, and Robust scaling, each suited for different data distributions.

When should I use normalization in my machine learning projects?

Normalization is particularly useful when your features have different units or scales, especially for algorithms sensitive to feature magnitudes, like KNN or SVM.