Evaluation Metrics for Machine Learning

accuracy | Accuracy |

ae | Absolute Error |

ape | Absolute Percent Error |

apk | Average Precision at k |

auc | Area under the ROC curve (AUC) |

bias | Bias |

ce | Classification Error |

f1 | F1 Score |

fbeta_score | F-beta Score |

ll | Log Loss |

logLoss | Mean Log Loss |

mae | Mean Absolute Error |

mape | Mean Absolute Percent Error |

mapk | Mean Average Precision at k |

mase | Mean Absolute Scaled Error |

mdae | Median Absolute Error |

MeanQuadraticWeightedKappa | Mean Quadratic Weighted Kappa |

mse | Mean Squared Error |

msle | Mean Squared Log Error |

params_binary | Inherit Documentation for Binary Classification Metrics |

params_classification | Inherit Documentation for Classification Metrics |

params_regression | Inherit Documentation for Regression Metrics |

percent_bias | Percent Bias |

precision | Precision |

rae | Relative Absolute Error |

recall | Recall |

rmse | Root Mean Squared Error |

rmsle | Root Mean Squared Log Error |

rrse | Root Relative Squared Error |

rse | Relative Squared Error |

ScoreQuadraticWeightedKappa | Quadratic Weighted Kappa |

se | Squared Error |

sle | Squared Log Error |

smape | Symmetric Mean Absolute Percentage Error |

sse | Sum of Squared Errors |

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