Feature Scaling: Normalization and Standardization
MinMaxScaler, StandardScaler, RobustScaler — when to use each and why it matters.
Why Scale Features?
Many algorithms are sensitive to feature MAGNITUDE. A feature ranging 0 to 1,000,000 will dominate one ranging 0 to 1 in distance- or gradient-based models. Scaling puts features on comparable ranges.
Who Needs Scaling
Scaling matters for KNN, SVM, k-means, PCA, and gradient-descent models like linear/logistic regression and neural networks. Tree-based models (random forests, gradient boosting) are scale-invariant and do not need it.
All lessons in this course
- Outlier Detection and Removal
- Encoding Categorical Variables
- Feature Scaling: Normalization and Standardization
- Building Preprocessing Pipelines