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:

  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}


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