What is the impact of feature scaling in machine learning?

What is the impact of feature scaling in machine learning?

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

Why do some algorithms require feature scaling?

Algorithms like KNN and SVM are sensitive to the scale of data, so scaling helps improve their performance and accuracy.

What are some common methods of feature scaling?

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

Can feature scaling affect model performance?

Yes, improper scaling can lead to poor model performance, as some features may dominate others.

When should I apply feature scaling?

Feature scaling should be applied during the preprocessing stage, especially before training models that are sensitive to feature scales.