Why is data scaling important in machine learning?

Why is data scaling important in machine learning?

Data scaling is crucial because it ensures that features contribute equally to the distance calculations in algorithms.

What are some common methods for scaling data?

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 between values.

Can scaling affect the performance of my model?

Yes, improper scaling can lead to poor model performance, especially in algorithms sensitive to feature magnitudes.