# MMSR

`crowdkit.aggregation.classification.m_msr.MMSR`

```
MMSR(
self,
n_iter: int = 10000,
eps: float = ...,
random_state: Optional[int] = 0,
observation_matrix: ndarray = ...,
covariation_matrix: ndarray = ...,
n_common_tasks: ndarray = ...,
n_performers: int = 0,
n_tasks: int = 0,
n_labels: int = 0,
labels_mapping: dict = ...,
performers_mapping: dict = ...,
tasks_mapping: dict = ...
)
```

Matrix Mean-Subsequence-Reduced Algorithm

Qianqian Ma and Alex Olshevsky. 2020. Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion 34th Conference on Neural Information Processing Systems (NeurIPS 2020) https://arxiv.org/abs/2010.12181

## Parameters Description

Parameters | Type | Description |
---|---|---|

`labels_` | Optional[Series] | Tasks labels A pandas.Series indexed by |

`skills_` | Optional[Series] | Performers skills A pandas.Series index by performers and holding corresponding performers skill |

`scores_` | Optional[DataFrame] | Tasks label scores A pandas.DataFrame indexed by |

Parameters | Type | Description |
---|---|---|

`labels_` | Optional[Series] | Tasks labels A pandas.Series indexed by |

`skills_` | Optional[Series] | Performers skills A pandas.Series index by performers and holding corresponding performers skill |

`scores_` | Optional[DataFrame] | Tasks label scores A pandas.DataFrame indexed by |

## Methods summary

Method | Description |
---|---|

fit | None |

fit_predict | None |

fit_predict_score | None |

predict | None |

predict_score | None |

Method | Description |
---|---|

fit | None |

fit_predict | None |

fit_predict_score | None |

predict | None |

predict_score | None |