The notion of similarity, and it’s complimentary counterpart distance, play key roles in many data science and machine learning projects, where similarity functions serve as methods by which we can group or separate objects and concepts and make generalisations across our data sets.

There are a vast array of different similarity metrics, which may be used in a variety of different scenarios. Very often it is possible to identify a similarity metric that has the desirable characteristics for a given problem space, and there are some great python functions which make such methods easily accessible. However my experience suggests that…

Dudek Pinchakov

Data Scientist at Alderley.ai

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