docs/evaluation/index.rst
Evaluating Recommender Output
=============================
LensKit's evaluation support is based on post-processing the output of recommenders
and predictors. The `batch utilities`_ provide support for generating these outputs.
We generally recommend using Jupyter_ notebooks for evaluation.
When writing recommender system evaluation results for publication, it's
important to be precise about how exactly your metrics are being computed
:cite:p:`Tamm2021-hz`; to aid with that, each metric function's documentation
includes a mathematical definition of the metric.
.. _batch utilities: batch.html
.. _Jupyter: https://jupyter.org
.. toctree::
:caption: Evaluation Topics
predict-metrics
topn-metrics
Saving and Loading Outputs
~~~~~~~~~~~~~~~~~~~~~~~~~~
In our own experiments, we typically store the output of recommendation runs in LensKit
experiments in CSV or Parquet files, along with whatever parameters are relevant from
the configuration.