# prediction_algorithms packageΒΆ

The `prediction_algorithms`

package includes the prediction algorithms
available for recommendation.

The available prediction algorithms are:

`random_pred.NormalPredictor` |
Algorithm predicting a random rating based on the distribution of the training set, which is assumed to be normal. |

`baseline_only.BaselineOnly` |
Algorithm predicting the baseline estimate for given user and item. |

`knns.KNNBasic` |
A basic collaborative filtering algorithm. |

`knns.KNNWithMeans` |
A basic collaborative filtering algorithm, taking into account the mean ratings of each user. |

`knns.KNNWithZScore` |
A basic collaborative filtering algorithm, taking into account the z-score normalization of each user. |

`knns.KNNBaseline` |
A basic collaborative filtering algorithm taking into account a baseline rating. |

`matrix_factorization.SVD` |
The famous SVD algorithm, as popularized by Simon Funk during the Netflix Prize.When baselines are not used, this is equivalent to Probabilistic Matrix Factorization [salakhutdinov2008a] (see note below).. |

`matrix_factorization.SVDpp` |
The SVD++ algorithm, an extension of `SVD` taking into account implicit ratings. |

`matrix_factorization.NMF` |
A collaborative filtering algorithm based on Non-negative Matrix Factorization. |

`slope_one.SlopeOne` |
A simple yet accurate collaborative filtering algorithm. |

`co_clustering.CoClustering` |
A collaborative filtering algorithm based on co-clustering. |

You may want to check the notation standards before diving into the formulas.