Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation

1 code implementation • 13 Sep 2021 • Yuxing Han, Ziniu Wu, Peizhi Wu, Rong Zhu, Jingyi Yang, Liang Wei Tan, Kai Zeng, Gao Cong, Yanzhao Qin, Andreas Pfadler, Zhengping Qian, Jingren Zhou, Jiangneng Li, Bin Cui

Therefore, we propose a new metric P-Error to evaluate the performance of CardEst methods, which overcomes the limitation of Q-Error and is able to reflect the overall end-to-end performance of CardEst methods.

Partially Interpretable Estimators (PIE): Black-Box-Refined Interpretable Machine Learning

no code implementations • 6 May 2021 • Tong Wang, Jingyi Yang, Yunyi Li, Boxiang Wang

We propose Partially Interpretable Estimators (PIE) which attribute a prediction to individual features via an interpretable model, while a (possibly) small part of the PIE prediction is attributed to the interaction of features via a black-box model, with the goal to boost the predictive performance while maintaining interpretability.

Attribute BIG-bench Machine Learning +1