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- research-articleAugust 2015
Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission
KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data MiningPages 1721–1730https://doi.org/10.1145/2783258.2788613In machine learning often a tradeoff must be made between accuracy and intelligibility. More accurate models such as boosted trees, random forests, and neural nets usually are not intelligible, but more intelligible models such as logistic regression, ...
- research-articleAugust 2015
ALOJA-ML: A Framework for Automating Characterization and Knowledge Discovery in Hadoop Deployments
KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data MiningPages 1701–1710https://doi.org/10.1145/2783258.2788600This article presents ALOJA-Machine Learning (ALOJA-ML) an extension to the ALOJA project that uses machine learning techniques to interpret Hadoop benchmark performance data and performance tuning; here we detail the approach, efficacy of the model and ...
- research-articleAugust 2015
A Decision Tree Framework for Spatiotemporal Sequence Prediction
KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data MiningPages 577–586https://doi.org/10.1145/2783258.2783356We study the problem of learning to predict a spatiotemporal output sequence given an input sequence. In contrast to conventional sequence prediction problems such as part-of-speech tagging (where output sequences are selected using a relatively small ...
