Different types of distances in statistics
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In statistics, probability theory, andinformation theory, a statistical distancequantifies the distance between two statistical objects, which can be two random variables, or two probability distributions orsamples, or the distance can be between an individual sample point and a population or a wider sample of points.
A distance between populations can be interpreted as measuring the distance between two probability distributions and hence they are essentially measures of distances between probability measures. Where statistical distance measures relate to the differences between random variables, these may have statistical dependence,[1] and hence these distances are not directly related to measures of distances between probability measures. Again, a measure of distance between random variables may relate to the extent of dependence between them, rather than to their individual values.
Statistical distance measures are mostly notmetrics and they need not be symmetric. Some types of distance measures are referred to as (statistical) divergences
A distance between populations can be interpreted as measuring the distance between two probability distributions and hence they are essentially measures of distances between probability measures. Where statistical distance measures relate to the differences between random variables, these may have statistical dependence,[1] and hence these distances are not directly related to measures of distances between probability measures. Again, a measure of distance between random variables may relate to the extent of dependence between them, rather than to their individual values.
Statistical distance measures are mostly notmetrics and they need not be symmetric. Some types of distance measures are referred to as (statistical) divergences
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We will walk through 4 types of distance metrics in machine learning and understand how they work in Python.
4 Types of Distance Metrics in Machine Learning. Euclidean Distance. ...
Euclidean Distance. Euclidean Distance represents the shortest distance between two points. ...
Manhattan Distance. ...
Minkowski Distance. ...
Hamming Distance.
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