[
https://issues.apache.org/jira/browse/SPARK-1359?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15219190#comment-15219190
]
Yu Ishikawa commented on SPARK-1359:
------------------------------------
[~mbaddar] Since the current ann in mllib depends on `GradientDescent`, we
should modify the efficienty.
How do we evaluate new implementation against the current implementation? And
What are better tasks to evaluate it?
- Metrics
1. Convergence Effieiency
2. Compute Cost
3. Compute Time
4. Other
- Task
1. Logistic Regression and Linear Regression with random generated data
2. Logistic Regression and Linear Regression with any Kaggle data
3. Other
I make an implementation of Parallelized Stochastic Gradient Descent.
https://github.com/yu-iskw/spark-parallelized-sgd
> SGD implementation is not efficient
> -----------------------------------
>
> Key: SPARK-1359
> URL: https://issues.apache.org/jira/browse/SPARK-1359
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 0.9.0, 1.0.0
> Reporter: Xiangrui Meng
>
> The SGD implementation samples a mini-batch to compute the stochastic
> gradient. This is not efficient because examples are provided via an iterator
> interface. We have to scan all of them to obtain a sample.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
