Why is data normalization important?

Why is data normalization important in machine learning?

Data normalization is crucial because it ensures that different features contribute equally to the distance calculations, improving model performance.

Can you explain how normalization affects model accuracy?

Normalization can significantly enhance model accuracy by reducing bias towards features with larger ranges, leading to better generalization.

What are some common methods of data normalization?

Common methods include Min-Max scaling, Z-score normalization, and Robust scaling, each serving different data distributions.

When should I normalize my data?

You should normalize your data when features have different scales or when using algorithms sensitive to feature magnitudes, like KNN or SVM.