3 papers
cs.AI2020
Evaluating Tree Explanation Methods for Anomaly Reasoning: A Case Study of SHAP TreeExplainer and TreeInterpreter
Pulkit Sharma, Shezan Rohinton Mirzan, Apurva Bhandari +4
Understanding predictions made by Machine Learning models is critical in many applications. In this work, we investigate the performance of two methods for explaining tree-based mo…
cs.LG2019
Griffon: Reasoning about Job Anomalies with Unlabeled Data in Cloud-based Platforms
Liqun Shao, Yiwen Zhu, Abhiram Eswaran +9
Microsoft's internal big data analytics platform is comprised of hundreds of thousands of machines, serving over half a million jobs daily, from thousands of users. The majority of…
cs.LG2018
MMLSpark: Unifying Machine Learning Ecosystems at Massive Scales
Mark Hamilton, Sudarshan Raghunathan, Ilya Matiach +13
We introduce Microsoft Machine Learning for Apache Spark (MMLSpark), an ecosystem of enhancements that expand the Apache Spark distributed computing library to tackle problems in D…