Machine learning blocks

This work presents MLBlocks, a machine learning system that lets data scientists explore the space of modeling techniques in a very easy and efficient manner. We show how the system is very general in the sense that virtually any problem and dataset can be casted to use MLBlocks, and how it supports the exploration of Discriminative Modeling, Generative Modeling and the use of synthetic features to boost performance. MLBlocks is highly parameterizable, and some of its powerful features include the ease of formulating lead and lag experiments for time series data, its simple interface for automation, and its extensibility to additional modeling techniques. We show how we used MLBlocks to quickly get results for two very different realworld data science problems. In the first, we used time series data from Massive Open Online Courses to cast many lead and lag formulations of predicting student dropout. In the second, we used MLBlocks' Discriminative Modeling functionality to find the best-performing model for predicting the destination of a car given its past trajectories. This later functionality is self-optimizing and will find the best model by exploring a space of 11 classification algorithms with a combination of Multi-Armed Bandit strategies and Gaussian Process optimizations, all in a distributed fashion in the cloud.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2015

Author dc:creator

• Collazo Santiago, Bryan Omar

Advisor dc:contributor.advisor

• Kalyan Veeramachaneni.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/100301

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/100301