Feature Scaling
Feature scaling is a data preprocessing technique used in machine learning and statistics to standardize or normalize the range of independent variables (features) in a dataset. It transforms the values of features to a common scale, typically between 0 and 1 or with a mean of 0 and standard deviation of 1, to ensure that no single feature dominates others due to differences in magnitude. This process helps improve the performance and convergence speed of many machine learning algorithms, such as gradient descent-based models, distance-based algorithms, and neural networks.
Developers should learn and use feature scaling when working with machine learning models that are sensitive to the scale of input features, such as support vector machines, k-nearest neighbors, and linear regression with regularization. It is essential in scenarios where features have different units or ranges (e.g., age in years vs. income in dollars) to prevent algorithms from being biased toward features with larger values. For example, in image processing or natural language processing, scaling pixel intensities or word frequencies ensures consistent model training and better predictive accuracy.