Overview
Many machine learning algorithms (like K-Means, SVM, and Neural Networks) are sensitive to the scale of the input data. Feature scaling ensures that all features contribute equally to the model's performance.
Common Methods
- Normalization (Min-Max Scaling): Rescaling data to a range of [0, 1].
- Standardization (Z-score Normalization): Rescaling data to have a mean of 0 and a standard deviation of 1.
Importance
Without scaling, features with larger numerical ranges (e.g., income) can dominate features with smaller ranges (e.g., age), leading to biased models and slower convergence during training.