docstr: Single user and item prediction.
docstr_tok: ["Single","user","and","item","prediction","."]
code: def predict(self, u, i):
      """Single user and item prediction."""
      prediction = (
          self.global_bias + self.user_bias[u] + self.item_bias[i]
      )
      prediction += self.user_vecs[u, :].dot(self.item_vecs[i, :].T)
      return prediction
  
file_path: fancyimpute/fancyimpute/matrix_factorization.py
imports: import numpy
  from sklearn.utils import check_array
  from .solver import Solver
  from .common import masked_mae
label_desc: Use image node_id 4 for calling the MatrixFactorization obj's underlying member method code with example usage: obj.predict(u, i) and returns: prediction