[webmaven]

Sometimes it is just a case of borrowing terms from one of several overlapping fields of knowledge as opposed to using a different term from one of the others. This is happening quite a lot in ML recently as ML borrows tools from classical AI, statistics (along with the arguments between Bayesians and frequentists), graph theory, information theory, game theory, neuroscience, software development, various branches of mathematics and geometry, and so on.

Borrowing the geometry-flavored term vector for the representation of a data point constituting a multidimensional magnitude+direction value[1] as opposed to the term explanatory variable from linear algebra/statistics is just happenstance, and is not an invention of a new term (or even a case of applying an existing term to something new).

Feature vectors and explanatory variables are, quite simply, two labels for the very same thing[2][3], and are often used interchangeably depending on context.

As for the familiarity argument, thinking of the data points as vectors is actually quite useful as a framing device for problem statements in terms of the tools and solutions it suggests[4].

[1] https://en.wikipedia.org/wiki/Feature_vector

[2] https://en.wikipedia.org/wiki/Vector_space

[3] https://en.wikipedia.org/wiki/Dependent_and_independent_vari...

[4] https://www.technologyreview.com/s/602025/how-vector-space-m...