In this recipe, we will illustrate how to use TensorFlow's text distance metric, the Levenshtein distance (the edit distance), between strings. This will be important later in this chapter, as we expand the nearest-neighbor methods to include features with text.
The Levenshtein distance is the minimal number of edits to get from one string to another string. The allowed edits are inserting a character, deleting a character, or substituting a character with a different one. For this recipe, we will use TensorFlow's Levenshtein distance function, edit_distance(). It is worthwhile to illustrate the use of this function, because its use will be applicable in later chapters.
Note that TensorFlow's edit_distance() function only accepts ...
