class_docstr: Biased matrix factorization for implicit feedback using SciKit-Learn's SVD
  solver (:class:`sklearn.decomposition.TruncatedSVD`).  It operates by first
  computing the bias, then computing the SVD of the bias residuals.
  
  You'll generally want one of the iterative SVD implementations such as
  :class:`lennskit.algorithms.als.BiasedMF`; this is here primarily as an
  example and for cases where you want to evaluate a pure SVD implementation.
code: def __str__(self):
      return f"BiasedSVD({self.factorization})"
  
file_path: lkpy/lenskit/algorithms/svd.py
imports: import logging
  import pandas
  import numpy
  from .None import Predictor
  from .bias import Bias
  from ..data import sparse_ratings
  from ..util import Stopwatch
label_desc: Use image node_id 5 for calling the BiasedSVD obj's underlying member method code with example usage: obj.__str__() and returns: str, self, str