Peter Rudenko created SPARK-10870:
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Summary: Criteo Display Advertising Challenge dataset
Key: SPARK-10870
URL: https://issues.apache.org/jira/browse/SPARK-10870
Project: Spark
Issue Type: Sub-task
Reporter: Peter Rudenko
Very useful dataset to test pipeline because of:
# "Big data" dataset - original Kaggle competition dataset is 12 gb, but
there's [1tb|http://labs.criteo.com/downloads/download-terabyte-click-logs/]
dataset of the same schema as well.
# Sparse models - categorical features has high cardinality
# Reproducible results - because it's public and many other distributed machine
learning libraries (e.g.
[wormwhole|https://github.com/dmlc/wormhole/blob/master/doc/tutorial/criteo_kaggle.rst],
[parameter
server|https://github.com/dmlc/parameter_server/blob/master/example/linear/criteo/README.md]
etc.) have made a base line benchmarks on which we could compare.
I have some base line results with custom models (GBDT encoders and tuned LR)
on spark-1.4. Will make pipelines using public spark model. [Winning
solution|http://www.csie.ntu.edu.tw/~r01922136/kaggle-2014-criteo.pdf] used
GBDT encoder (not available in spark, but not difficult to make one from GBT
from mllib) + hashing + factorization machine (planned for spark-1.6).
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