This sounds much better.
Follow up question is if we should provide MAP@k, which I believe is
wider used metric.
On 12/06/2016 09:52 PM, Sean Owen wrote:
> As I understand, this might best be called "mean precision@k", not
> "mean average precision, up to k".
>
> On Tue, Dec 6, 2016 at 9:43 PM M
As I understand, this might best be called "mean precision@k", not "mean
average precision, up to k".
On Tue, Dec 6, 2016 at 9:43 PM Maciej Szymkiewicz
wrote:
> Thank you Sean.
>
> Maybe I am just confused about the language. When I read that it returns "the
> average precision at the first k ra
Thank you Sean.
Maybe I am just confused about the language. When I read that it returns
"the average precision at the first k ranking positions" I somehow
expect there will ap@k there and a the final output would be MAP@k not
average precision at the k-th position.
I guess it is not enough sleep
I read it again and that looks like it implements mean precision@k as I
would expect. What is the issue?
On Tue, Dec 6, 2016, 07:30 Maciej Szymkiewicz
wrote:
> Hi,
>
> Could I ask for a fresh pair of eyes on this piece of code:
>
>
> https://github.com/apache/spark/blob/f830bb9170f6b853565d9dd30
Hi,
Could I ask fora fresh pair of eyes on this piece of code:
https://github.com/apache/spark/blob/f830bb9170f6b853565d9dd30ca7418b93a54fe3/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala#L59-L80
@Since("1.2.0")
def precisionAt(k: Int): Double = {
require(k >
