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https://issues.apache.org/jira/browse/SPARK-9941?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng updated SPARK-9941:
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Description:
This is an umbrella JIRA to track some fun tasks :)
We have built many features under the ML pipeline API, and we want to see how
it works on real-world datasets, e.g., Kaggle competition datasets
(https://www.kaggle.com/competitions). We want to invite community members to
help test. The goal is NOT to win the competitions but to provide code examples
and to find out missing features and other issues to help shape the roadmap.
For people who are interested, please do the following:
1. Create a subtask (or leave a comment if you cannot create a subtask) to
claim a Kaggle dataset.
2. Use the ML pipeline API to build and tune an ML pipeline that works for the
Kaggle dataset.
3. Paste the code to gist (https://gist.github.com/) and provide the link here.
4. Report missing features, issues, running times, and accuracy.
was:
This is an umbrella JIRA to track some fun tasks :)
We have built many features under the ML pipeline API, and we want to see how
it works on real-world datasets, e.g., Kaggle competition datasets. We want to
invite community members to help test. The goal is NOT to win the competitions
but to provide code examples and to find out missing features and other issues
to help shape the roadmap.
For people who are interested, please do the following:
1. Create a subtask (or leave a comment if you cannot create a subtask) to
claim a Kaggle dataset.
2. Use the ML pipeline API to build and tune an ML pipeline that works for the
Kaggle dataset.
3. Paste the code to gist (https://gist.github.com/) and provide the link.
4. Report missing features, issues, running times, and accuracy.
> Try ML pipeline API on Kaggle competitions
> ------------------------------------------
>
> Key: SPARK-9941
> URL: https://issues.apache.org/jira/browse/SPARK-9941
> Project: Spark
> Issue Type: Umbrella
> Components: ML
> Reporter: Xiangrui Meng
> Assignee: Xiangrui Meng
>
> This is an umbrella JIRA to track some fun tasks :)
> We have built many features under the ML pipeline API, and we want to see how
> it works on real-world datasets, e.g., Kaggle competition datasets
> (https://www.kaggle.com/competitions). We want to invite community members to
> help test. The goal is NOT to win the competitions but to provide code
> examples and to find out missing features and other issues to help shape the
> roadmap.
> For people who are interested, please do the following:
> 1. Create a subtask (or leave a comment if you cannot create a subtask) to
> claim a Kaggle dataset.
> 2. Use the ML pipeline API to build and tune an ML pipeline that works for
> the Kaggle dataset.
> 3. Paste the code to gist (https://gist.github.com/) and provide the link
> here.
> 4. Report missing features, issues, running times, and accuracy.
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