What are the effects of normalization in data processing?

What are the effects of normalization in data processing?

Normalization helps in scaling data, improving model performance, and ensuring that features contribute equally.

Can you explain how normalization impacts machine learning models?

Normalization can lead to faster convergence in training and can prevent models from being biased towards certain features.

Are there different methods of normalization?

Yes, common methods include Min-Max scaling, Z-score normalization, and Robust scaling, each serving different purposes.

When should I use normalization?

Normalization is particularly useful when features have different units or scales, especially in algorithms sensitive to these differences.

What are the drawbacks of normalization?

Drawbacks include potential loss of information and the assumption that data follows a specific distribution.