Description:

Hivemall provides machine learning functionality as well as feature engineering functions through UDFs/UDAFs/UDTFs of Hive. It is designed to be scalable to the number of training instances as well as the number of training features.

Though we consider that Hivemall is much easier to use and more scalable than Mahout for classification/regression tasks, please check it by yourself. If you have a Hive environment, you can evaluate Hivemall within 5 minutes or so.

Hivemall is very easy to use as every machine learning step is done within HiveQL.

-- Installation is just as follows:
add jar /tmp/hivemall.jar; source /tmp/define-all.hive;

-- Logistic regression is performed by a query.
SELECT feature, avg(weight) as weight FROM ( SELECT logress(features,label) as (feature,weight) FROM training_features ) t GROUP BY feature;

You can find detailed examples on our wiki pages.

Changes to previous version:

• Supported Matrix Factorization
• Added a support for TF-IDF computation
• Supported AdaGrad/AdaDelta
• Supported AdaGradRDA classification
• Added normalization scheme

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Linux

Data Formats: Csv, Any, Tsv

Tags: Classification, Regression, Online Learning, Matrix Factorization, Logistic Regression, Multiclass Classification, Recommendation, Hadoop, Hive, Passive Aggressive, Confidence Weighted, Adagrad

Archive: download here

Other available revisons

Version	Changelog	Date
0.3	Supported Matrix Factorization Added a support for TF-IDF computation Supported AdaGrad/AdaDelta Supported AdaGradRDA classification Added normalization scheme	March 13, 2015, 17:08:22
0.1	Enhancement Added AROW regression Added AROW with a hinge loss (arowh_regress()) Bugfix Fixed a bug of null feature handling in classification/regression	October 25, 2013, 08:43:12
0.1-rc3	Add support for the state-of-the-art classifiers (CW, AROW, SCW).	October 9, 2013, 09:25:41
0.1	Initial release candidate.	October 3, 2013, 13:23:24

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