Why is it important to standardize features before modeling?

Standardizing features is crucial before modeling, especially for algorithms that rely on distance calculations, such as k-nearest neighbors and support vector machines. When features are on different scales, those with larger ranges can disproportionately influence the outcome of the model. Standardization adjusts the features so that they have a mean of zero and a standard deviation of one, ensuring that each feature contributes equally to the distance measures used in these algorithms. This equal contribution allows the model to capture the true relationships between the features and the target variable more effectively, leading to better performance.

While simplifying the model training process and enhancing interpretability are valuable aspects in modeling, they are not the primary reasons for standardizing features. Reducing dimensionality involves techniques like PCA (Principal Component Analysis) rather than standardization. Therefore, the primary rationale for standardizing features is to ensure equality in their contribution to distance calculations in sensitive algorithms.