In some articles, it's said knn uses hamming distance for one-hot encoded categorical variables. Does the scikit learn implementation of knn follow the same way.
Also are there any other ways to handle categorical input variables when using knn.
1 Answer
$\begingroup$ $\endgroup$
2 As stated in the docs, the KNeighborsClassifier from scikit-learn uses minkowski distance by default.
Other metrics can be used, and you can probably get a decent idea by looking at the docs for scikit-learn's DistanceMetric class
- $\begingroup$ ok. minkowski distance can be used for continuous input variables. but what about discrete/categorical input variables? $\endgroup$insomniac– insomniac2022-01-19 06:22:40 +00:00Commented Jan 19, 2022 at 6:22
- $\begingroup$ No free lunch, so it's hard to give a straight answer. But I would experiment with 'jaccard' and 'matching'. They are also in the docs, so should work like a charm. $\endgroup$Tim J– Tim J2022-06-28 11:47:52 +00:00Commented Jun 28, 2022 at 11:47