# Quality Report¶

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

## 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.

Table 10. Errors/Estimators types

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.