Description: Feature vector normalization is a fundamental process in data preprocessing that aims to adjust feature vectors to ensure they are on a similar scale. This adjustment is crucial because many machine learning techniques and data analysis methods are sensitive to the scale of the data. When features have different ranges, some may dominate the learning process, leading to biased or inaccurate results. Normalization can involve techniques such as min-max scaling, which transforms data to fall within a specific range, or Z-score normalization (standardization), which adjusts data to have a mean of zero and a standard deviation of one. By applying these techniques, the convergence of optimization algorithms is facilitated, and the accuracy of predictive models is improved. Additionally, normalization allows for better interpretation of results, as features are presented on a comparable scale. In summary, feature vector normalization is an essential step in data preprocessing that helps ensure machine learning models operate effectively and efficiently.
