Hi Tsai,
Thank you for pointing out the implementation details which I missed.
Yes I saw several jira issues with the intercept, regularization and
standardization, I just didn't realize it made such a big impact.
Thanks again.
2015-10-13 4:32 GMT+08:00 DB Tsai :
> Hi Liu,
>
> In ML, even after e
Hi Liu,
In ML, even after extracting the data into RDD, the versions between MLib
and ML are quite different. Due to legacy design, in MLlib, we use Updater
for handling regularization, and this layer of abstraction also does
adaptive step size which is only for SGD. In order to get it working wit
Hi Joseph,
Thank you for clarifying the motivation that you setup a different API
for ml pipelines, it sounds great. But I still think we could extract
some common parts of the training & inference procedures for ml and
mllib. In ml.classification.LogisticRegression, you simply transform
the DataF
Hi YiZhi Liu,
The spark.ml classes are part of the higher-level "Pipelines" API, which
works with DataFrames. When creating this API, we decided to separate it
from the old API to avoid confusion. You can read more about it here:
http://spark.apache.org/docs/latest/ml-guide.html
For (3): We use
Hi everyone,
I'm curious about the difference between
ml.classification.LogisticRegression and
mllib.classification.LogisticRegressionWithLBFGS. Both of them are
optimized using LBFGS, the only difference I see is LogisticRegression
takes DataFrame while LogisticRegressionWithLBFGS takes RDD.
So