What is the importance of scaling in machine learning?

What is the importance of scaling in machine learning?

Scaling is crucial in machine learning as it ensures that features contribute equally to the model's performance.

Can you explain why features need to be on a similar scale?

When features are on different scales, models may become biased towards features with larger ranges, leading to poor performance.

What are some common methods for scaling data?

Common methods include Min-Max scaling, Standardization (Z-score normalization), and Robust scaling.

How does scaling affect model training time?

Scaling can significantly reduce training time, especially for algorithms like gradient descent, by improving convergence rates.

Are there any exceptions where scaling isn't necessary?

Yes, some tree-based algorithms like Decision Trees and Random Forests are not sensitive to feature scaling.