Alternatively, I will give a talk about LOR and LIR with elastic-net
implementation and interpretation of those models in spark summit.
https://spark-summit.org/2015/events/large-scale-lasso-and-elastic-net-regularized-generalized-linear-models/
You may attend or watch online.
Sincerely,
DB
I hope they will come up with1.4 before spark summit in mid June
On 31 May 2015 10:07, Joseph Bradley jos...@databricks.com wrote:
Spark 1.4 should be available next month, but I'm not sure about the exact
date.
Your interpretation of high lambda is reasonable. High lambda is really
Spark 1.4 should be available next month, but I'm not sure about the exact
date.
Your interpretation of high lambda is reasonable. High lambda is really
data-dependent.
lambda is the same as the regParam in Spark, available in all recent
Spark versions.
On Fri, May 29, 2015 at 5:35 AM, mélanie
When will Spark 1.4 be available exactly?
To answer to Model selection can be achieved through high
lambda resulting lots of zero in the coefficients : Do you mean that
putting a high lambda as a parameter of the logistic regression keeps only
a few significant variables and deletes the others
I am new in MLlib and in Spark.(I use Scala)
I'm trying to understand how LogisticRegressionWithLBFGS and
LogisticRegressionWithSGD work.
I usually use R to do logistic regressions but now I do it on Spark
to be able to analyze Big Data.
The model only returns weights and intercept. My problem
In Spark 1.4, Logistic Regression with elasticNet is implemented in ML
pipeline framework. Model selection can be achieved through high
lambda resulting lots of zero in the coefficients.
Sincerely,
DB Tsai
---
Blog: https://www.dbtsai.com
On
If you want to select specific variable combinations by hand, then you will
need to modify the dataset before passing it to the ML algorithm. The
DataFrame API should make that easy to do.
If you want to have an ML algorithm select variables automatically, then I
would recommend using L1
