Why is feature scaling important in machine learning?

Why is feature scaling important in machine learning?

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

What happens if I don't scale my features?

If you don't scale your features, algorithms like K-means or gradient descent may perform poorly, leading to inaccurate models.

Can you give me examples of feature scaling techniques?

Sure! Common techniques 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 features.

Are there any downsides to feature scaling?

Yes, scaling can sometimes distort the relationships in the data, especially if outliers are present.