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:

Add support for the state-of-the-art classifiers (CW, AROW, SCW).

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Linux

Data Formats: Csv, Any, Tsv

Tags: Classification, Regression, Online Learning, Logistic Regression, Multiclass Classification, Hadoop, Hive, Passive Aggressive, Open Source, Confidence Weighted

Archive: download here

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