Hi Joseph,
I've just tried that out. MLLib indeed returns different models. I see
no problem here then. How can Filipp's issue be possible?
Best,
Valeriy.
On 04/27/2018 10:00 PM, Valeriy Avanesov wrote:
Hi all,
maybe I'm missing something, but from what was discussed here
standardization will get different result.
But, if comparing results between R-glmnet and mllib, if we set the
same parameters for regularization/standardization/... , then we
should get the same result. If not, thenmaybe there's a bug. In this
case you can paste your testing code and I can hel
Hi all.
Filipp, do you use l1/l2/elstic-net penalization? I believe in this case
standardization matters.
Best,
Valeriy.
On 04/17/2018 11:40 AM, Weichen Xu wrote:
Not a bug.
When disabling standadization, mllib LR will still do standadization
for features, but it will scale the coefficie
owse/SPARK-23437
All concerned are welcome to discuss.
Best,
Valeriy.
On Sat, Feb 3, 2018 at 9:24 PM, Valeriy Avanesov <mailto:acop...@gmail.com>> wrote:
Hi,
no, I don't thing we should actually compute the n \times n
matrix. Leave alone inverting it. However, vari
s,
S
Am 01.02.18 um 20:01 schrieb Valeriy Avanesov:
Hi all,
it came to my surprise that there is no implementation of Gaussian
Process in Spark MLlib. The approach is widely known, employed and
scalable (its sparse versions). Is there a good reason for that? Has
it been discussed before?
If
Hi all,
it came to my surprise that there is no implementation of Gaussian
Process in Spark MLlib. The approach is widely known, employed and
scalable (its sparse versions). Is there a good reason for that? Has it
been discussed before?
If there is a need in this approach being a part of MLl
