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 get_params(self, deep=True):
      params = {
          "features": self.factorization.n_components,
          "algorithm": self.factorization.algorithm,
      }
      if deep and self.bias:
          for k, v in self.bias.get_params(True).items():
              params["bias__" + k] = v
      else:
          params["bias"] = self.bias
      return params
  
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 4 for calling the BiasedSVD obj's underlying member method code with example usage: obj.get_params(deep) and returns: params