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 happens if I don't scale my data?

If you don't scale your data, some features may dominate others, leading to poor model performance and inaccurate predictions.

Which scaling methods are commonly used?

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

Can you give an example of when to use each method?

Sure! Use Min-Max scaling for algorithms sensitive to the scale, Standardization for normally distributed data, and Robust scaling for data with outliers.

How does scaling affect model training time?

Scaling can significantly reduce training time, especially for algorithms like gradient descent, as it helps converge faster.