# Euclidean Distance - $d = \sqrt{\Sigma_{i=1}^{n}(p_{i}-q_{i})^{2}}$ - It is a distance measure that best can be explained as the length of a segment connecting two points. - calculated from the cartesian coordinates of the points using the Pythagorean theorem - Euclidean distance is not scale in-variant which means that distances computed might be skewed depending on the units of the [Features](Features.md). Typically, one needs to normalize the data before using this distance measure. - Moreover, as the dimensionality increases of your data, the less useful Euclidean distance becomes. This has to do with the [Curse Of Dimensionality](Curse%20Of%20Dimensionality.md) - works great when you have low-dimensional data and the magnitude of the vectors is important to be measured