RankEval – Analysis and evaluation of Learning-to-Rank models¶

RankEval is a Python library for the analysis and evaluation of Learning-to-Rank models based on ensembles of regression trees. Target audience includes the machine learning (ML) and information retrieval (IR) communities.

Citing RankEval¶

Please cite:

@inproceedings{rankeval-sigir17,
  author = {Claudio Lucchese and Cristina Ioana Muntean and Franco Maria Nardini and
            Raffaele Perego and Salvatore Trani},
  title = {RankEval: An Evaluation and Analysis Framework for Learning-to-Rank Solutions},
  booktitle = {SIGIR 2017: Proceedings of the 40th International {ACM} {SIGIR}
               Conference on Research and Development in Information Retrieval},
  year = {2017},
  location = {Tokyo, Japan}
}

Contents:

rankeval package

Indices and tables¶