Euclidean Distance
Euclidean distance is a measure of the distance between two points in a multi-dimensional space. It is calculated as the square root of the sum of the squared differences between the corresponding coordinates of the two points. In data science, Euclidean distance is commonly used as a similarity measure between two data points. It is used in clustering algorithms, such as k-means, to group similar data points together. It is also used in dimensionality reduction techniques, such as principal component analysis, to identify the most important features of a dataset. Additionally, Euclidean distance is used in machine learning algorithms, such as k-nearest neighbors, to classify new data points based on their similarity to existing data points.
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