[GitHub] [spark] zhengruifeng commented on issue #26124: [SPARK-29224][ML]Implement Factorization Machines as a ml-pipeline component

zhengruifeng commented on issue #26124: [SPARK-29224][ML]Implement Factorization Machines as a ml-pipeline component URL: https://github.com/apache/spark/pull/26124#issuecomment-565311614 LGTM. @mob-ai You can open another ticket for doc & examples, after this pr get merged. ---

[GitHub] [spark] zhengruifeng commented on issue #26124: [SPARK-29224][ML]Implement Factorization Machines as a ml-pipeline component

zhengruifeng commented on issue #26124: [SPARK-29224][ML]Implement Factorization Machines as a ml-pipeline component URL: https://github.com/apache/spark/pull/26124#issuecomment-561462028 > I still doubt whether existing testsuite is enough. 1, I suggest to add several testsuites to

[GitHub] [spark] zhengruifeng commented on issue #26124: [SPARK-29224][ML]Implement Factorization Machines as a ml-pipeline component

zhengruifeng commented on issue #26124: [SPARK-29224][ML]Implement Factorization Machines as a ml-pipeline component URL: https://github.com/apache/spark/pull/26124#issuecomment-547246258 In practice I am using FM/FFM, and IMHO SSP or ASYNC solvers (like Difacto/PS-lite) seems more effici