# Is a KNN-Classifier memory intensive?

In my opinion it seems fairly obvious that a $k$ nearest neighbours (KNN) Classifier would be fairly expensive in terms of memory, as the model is the training set itself. However, any notes I've read have only mentioned that the KNN-Classifier is computationally expensive, and have not mentioned any memory requirements as a disadvantage. I assume this to be true, but I wouldn't want to mention it in any exams or anything without proper confirmation. Are the memory requirements implicit and not worth mentioning, or am I overlooking something?

• You seem to be assuming that "computationally expensive" refers only to running time and not memory. I'm not sure that's a good assumption. – David Richerby Jan 6 '15 at 14:56
• I did consider this upon writing the question, but upon looking up the definition, it did still seem to refer to just the running time. However, it's probably sensible to assume that computationally expensive does actually refer to the memory requirements as well, which would explain why there is no mention of memory in any notes I've read. I'll take this as an answer unless someone can provide proper clarification. – Sammdahamm Jan 6 '15 at 15:01