# Quality Report¶

Clicking on the *Quality Report* icon shows **performance indicators** for each of the selected models.

Table of Contents

## Structure¶

Each model is evaluated for all available subsets (these are typically runs or experiments) within the **training/validation** and **test set**. Each subset (run/experiment)
is displayed within a different tab.

Note

The tab name is identical to the subset name being summarized.

Each **row** represents a different model.

## Errors and estimators types¶

The following **errors** and **estimators** are provided for each available model-subset pair for both the training/validation and the test set.

Tag |
Name |
---|---|

BIC |
Bayesian information criterion |

AICC |
AIC with a correction for small sample sizes |

AIC |
Akaike information criterion |

NRMSE |
Normalized root mean square error |

R2 |
Coefficient of determination (\(R^{2}\)) |

MallowsCp |
Mallows’s \(C_{p}\) |

FPE |
Akaike’s Final Prediction Error for estimated model |

MeanError |
Mean error |

StdError |
Standard error |

MaxAbsError |
Maximum absolute error |

MinAbsError |
Minimum absolute error |

SSE |
Sum of squared estimate of errors |

RMSE |
Root mean square error |

MSE |
Mean squared error |

NDIE |
— |

SAE |
Sum absolute error |

MAE |
Mean absolute error |

NMAE |
Normalized mean absolute error |

OUTPUTVARIANCE |
Output variance |

MODELOUTPUTVARIANCE |
Model output variance |

Note

If an error occurs during calculation due to zero division, etc. `NaN`

(*not a number*) will be displayed in the performance table.