lenskit.metrics.predict.RMSE
============================

.. py:class:: lenskit.metrics.predict.RMSE(missing_scores = 'error', missing_truth = 'error')
   

   Bases: :py:obj:`PredictMetric`


   Compute RMSE (root mean squared error).  This is computed as:

   .. math::
       \sum_{r_{ui} \in R} \left(r_{ui} - s(i|u)\right)^2

   This metric does not do any fallbacks; if you want to compute RMSE with
   fallback predictions (e.g. usign a bias model when a collaborative filter
   cannot predict), generate predictions with
   :class:`~lenskit.basic.FallbackScorer`.

   :Stability: Caller


   .. py:method:: measure_list(predictions, test = None, /)

      Compute measurements for a single list.

      :returns:

                - A float for simple metrics
                - Intermediate data for decomposed metrics
                - A dict mapping metric names to values for multi-metric classes



   .. py:method:: extract_list_metrics(data)

      Extract per-list metric(s) from intermediate measurement data.

      :returns:

                - A float for simple metrics
                - A dict mapping metric names to values for multi-metric classes
                - None if no per-list metrics are available



   .. py:method:: create_accumulator()

      Creaet an accumulator to aggregate per-list measurements into summary
      metrics.

      Each result from :meth:`measure_list` is passed to
      :meth:`Accumulator.add`.