Artificial Intelligence Programming Practice Exam 2025 - Free AI Programming Practice Questions and Study Guide

Feature scaling is a critical preprocessing step in machine learning aimed at normalizing the range of independent variables. When features in a dataset have different scales, models that rely on distance calculations, such as k-nearest neighbors or gradient descent optimization algorithms, may perform poorly. For example, if one feature ranges from 1 to 1000 and another from 0 to 1, the model may give disproportionate weight to the feature with the larger range.

By applying feature scaling techniques such as min-max scaling or standardization (z-score normalization), all features can be brought to a similar scale. Min-max scaling transforms features to a range between 0 and 1, while standardization reshapes the data to have a mean of 0 and a standard deviation of 1. This uniformity allows algorithms to converge more quickly and can result in more accurate predictions. Hence, normalizing the scales of independent variables through feature scaling ensures that the inherent relationships among the data can be effectively captured by machine learning algorithms.