Feature scaling should be used for algorithms that rely on Euclidean distance or gradient-based optimization, such as KNN, K-means, linear regression, logistic regression, and neural networks. It is generally not needed for tree-based models like decision trees, random forests, and XGBoost.
Conditions: Model uses distance metrics or iterative optimization sensitive to feature scale.; Model is not tree-based or an ensemble of trees.
Feature scaling should be used for algorithms that rely on Euclidean distance or gradient-based optimization, such as KNN, K-means, linear regression, logistic regression, and neural networks. It is generally not needed for tree-based models like decision trees, random forests, and XGBoost.
Conditions: Model uses distance metrics or iterative optimization sensitive to feature scale.; Model is not tree-based or an ensemble of trees.