Minkowski Distance
Minkowski Distance is a generalized distance metric in mathematics and data science that measures the distance between two points in a normed vector space. It is defined by a parameter p, which determines the specific type of distance (e.g., Manhattan distance for p=1, Euclidean distance for p=2, and Chebyshev distance as p approaches infinity). This metric is widely used in machine learning, clustering algorithms, and pattern recognition to quantify similarity or dissimilarity between data points.
Developers should learn Minkowski Distance when working on machine learning tasks that involve distance-based algorithms, such as k-nearest neighbors (KNN), k-means clustering, or similarity searches in high-dimensional data. It is particularly useful in data preprocessing, feature engineering, and optimization problems where flexible distance measures are needed, allowing customization through the p parameter to suit specific data characteristics or application requirements.