# Metrics v0.1.2

0

Monthly downloads

## Evaluation Metrics for Machine Learning

Metrics is a set of evaluation metrics that is commonly
used in supervised machine learning.

## Functions in Metrics

Name | Description | |

ll | Compute the log loss | |

mae | Compute the mean absolute error#' | |

mapk | Compute the mean average precision at k | |

logLoss | Compute the mean log loss | |

auc | Compute the area under the ROC (AUC) | |

ce | Compute the classification error | |

se | Compute the squared error | |

sle | Compute the squared log error | |

mse | Compute the mean squared error#' | |

msle | Compute the mean squared log error | |

MeanQuadraticWeightedKappa | Compute the mean quadratic weighted kappa | |

ScoreQuadraticWeightedKappa | Compute the quadratic weighted kappa | |

ae | Compute the absolute error#' | |

apk | Compute the average precision at k | |

rmse | Compute the root mean squared error#' | |

rmsle | Compute the root mean squared log error | |

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## Last month downloads

## Details

URL | https://github.com/benhamner/Metrics/tree/master/R |

License | BSD |

Collate | 'metrics.r' |

Packaged | 2017-04-21 14:50:30 UTC; ripley |

Repository | CRAN |

Date/Publication | 2017-04-21 14:55:21 UTC |

NeedsCompilation | no |

X-CRAN-Original-Maintainer | Ben Hamner |

X-CRAN-Comment | Orphaned and corrected on 2017-04-21 as check errors were not corrected despite reminders. |

suggests | RUnit |

Contributors | Ben Hamner |

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