Evaluate generator quality
MOSTLY AI calculates generator quality metrics after a generator finishes training. The metrics are available after you select the generator from the Generators pages. Further detailed charts and metrics are also available in the Model report.
Accuracy metrics
The generator overall accuracy is an average of the overall accuracies of all AI models in a generator.
An AI model's accuracy is an aggregated statistic that is the mean of the univariate accuracy, the bivariate accuracy, and (only for linked tables) the coherence of a model.
Univariate  The overall accuracy of the table’s univariate distributions. An aggregated statistic that shows the mean of the univariate accuracies of all columns. 
Bivariate  The overall accuracy of the table’s bivariate distributions. An aggregated statistic that shows the mean of the bivariate accuracies of all columns. 
Coherence  (Only for linked tables) The temporal coherence of time series data between the original and synthetic data as well as the preservation of the average sequence length (or the average number of linked table records that are related to a subject table record). 
You can find the detailed accuracy metrics for each table when you open the Model report.
Privacy metrics
MOSTLY AI generates synthetic data in a privacysafe way. The privacy of data subjects is protected as a result of the privacyprotection mechanisms that are in place for every generated synthetic dataset. For each new dataset, MOSTLY AI trains AI models that learn only the general patterns of the original dataset and never memorize data points. The AI models then generate data with random draws which is a process that ensures that no 1:1 relationship exists between the original and synthetic datasets. For more information, see Privacyprotection mechanisms.
In addition, MOSTLY AI calculates distance metrics for each trained model. These metrics assess whether the synthetic records are close, but not "too close" to the original records.
Distances
The Distances metric is a privacy metric that shows the average distances calculated between synthetic samples and their nearest neighbors from the training data, on one side, and between the synthetic samples and their nearest neighbors from the holdout data, on the other side (for reference).
To calculate the distances, 10% from the original data is randomly split into a holdout dataset. The Distance to Closest Record metric is then calculated like so and averaged for each table.
 DCR(S,T)  the distances between synthetic samples and their nearest neighbors from the training dataset
 DCR(S,H)  the distances between synthetic samples and their nearest neighbors from the holdout dataset (appears in light gray)
For the purposes of gauging privacy, the synthetic samples should be as close to their nearest training neighbors as they are close to their nearest holdout neighbors (the data not seen during training).
The full distribution of the distances is visualized as part of the Model report. See Distances in the Model report.
Technical reference
The Distances metric is calculated based on L2 distances between samples. Each sample is converted into JSON and is then mapped to a 384dimensional vector space with the allMiniLML6v2
(opens in a new tab) model so that the calculation takes into account semantic similarities. With this, the distances are applicable to both tabular mixedtype data as well as any unstructured text columns that you use to finetune LLMs.
Identical matches
An identical match is the special case of the distance measure being zero. In such a case, two records would share the same values across all columns. But also here, the occurrence of identical matches only represents a privacy issue if and only if it is more prevalent than within the training data itself. If the original dataset has identical matches, we can expect to see a similar number (but not significantly more) of identical matches in the synthetic data.
You can find the Identical matches metric in the model report.
Model report
For each model in a generator, you can open their Model report and view the metrics in detail. To open, click Model in the Reports column under Data insights.
MOSTLY AI generates a Model report for each model in a generator and a Data report for each model when you generate a synthetic dataset.
The Data report is especially important when you use data augmentation features, such as Rebalancing. For example, when you use Rebalancing, the changes in the distribution of the synthetic data will be visible only in the data report. For more information, see Evaluate synthetic data quality.
Each model contains a breakdown and visualization for the following quality metrics:
 Dataset statistics
 Correlations
 Univariate distributions
 Bivariate distributions
 Coherence (linked tables only)
 Accuracy
 Distances
To reduce computational time, the model report is calculated on a maximum of 100,000 samples that are randomly selected from the original and synthetic data. If smaller, the entire dataset is used.
Dataset statistics
At the top of the model report, the first card on the left shows the original dataset statistics of this model.

Dataset. The Dataset card shows dataset statistics that indicate the number of original samples used during training and the number of synthetic samples used to generate the report. For more information, see the table below.
Dataset statistics Description Original samples The number of original data samples (aka rows or records) Synthetic samples The number of synthetic data samples (aka rows or records) generated to calculate the model report Target columns The number of columns included in the model training Context columns The number of columns in the tables to which the current table has a context relationships 
Accuracy. The accuracy metrics as described in the Accuracy metrics section appear in the middle card of the top section.

Distances. The Distances and Identical Matches metrics as described in the Privacy section appear in the right card of the top section.
Correlations
The Correlations is the first available section in the model report.
This section shows three correlation matrices. They provide a way to assess whether the synthetic dataset retained the correlation structure of the original data set. This gives you an additional way to assess the quality of the synthetic data, but does not have an impact on the calculated overall accuracy score.
Both the X and Yaxis refer to the columns in the table that is evaluated, and each cell in the matrix correlates to a variable pair: the more two variables are correlated, the darker the cell becomes. This is obvious in the dark 45 degree diagonal which shows the correlations of single variables with themselves. Naturally, their correlation coefficient is 1. The third matrix shows the difference between the original and the synthetic data.
For subject tables, the charts display all columns.
For linked tables, the charts display the correlations between all columns as well as the correlations between each column and the Sequence Length.
Technical reference
The correlations are calculated by binning all variables into a maximum of 10 groups. For categorical columns, the 10 most common categories are used and for numerical columns, the deciles are chosen as cutoffs. Then, a correlation coefficient Φκ is calculated for each combination of variable pairs. The Φκ coefficient provides a stable solution when combining numerical and categorical variables and also captures nonlinear dependencies. The resulting correlation matrix is then colorcoded as a heatmap to indicate the strength of variable interdependencies, once for the actual and once for the synthetic data with scaling between 0 and 1.
Details about the Φκ coefficient can be found in the following paper: A new correlation coefficient between categorical, ordinal and interval variables with Pearson characteristics (opens in a new tab)
Univariate distributions
Univariate distributions describe the probability of a variable having a particular value. You can find four types of plots in this section of the QA report: categorical , continuous and datetime, but there’s also a Sequence Length plot if you synthesized a linked table.
For each variable, there’s a distribution and binned plot. These show the distributions of the original and the synthetic dataset in black and green, respectively. The percentage next to the title shows how accurately the original column is represented by the synthetic column.
You can search by column name to find a univariate chart.
You may find categories that are not present in the original dataset (for example, _RARE_
). These categories appear as a means of rare category protection, ensuring privacy protection of subjects with rare or unique features.
Technical reference
All variables are binned into a maximum of 10 groups. For categorical columns, the 10 most common categories are used and for numerical and datetime columns, the deciles are chosen as cutoffs. One additional group is used to show empty values: (empty) for categorical and (n/a) for numerical and datetime columns. This presented accuracy is the Manhattan distance (L1) calculated on the binned datasets.
Bivariate distributions
Bivariate distributions help you understand the conditional relationship between the content of two columns in your original dataset and how it is preserved in the synthetic dataset.
For each variable pair, there is a target and a synthetic plot. These show the relationships of the variables in the original and the synthetic dataset in black and green, respectively. Again, the percentage next to the title shows how accurately the original column is represented by the synthetic column.
The bivariate distribution below shows, for instance, that the age group of forty years and older is most likely to be married, and anyone below thirty is most likely to have never been married. You can see that this is the same in the synthetic dataset.
If it’s a QA report for a linked table, then you can find the plots with the context table’s columns by looking for context:[columnname]
. The word context here refers to either the subject table or another linked table with which this linked table has been synthesized.
You can search by column name to find a bivariate chart.
You may find categories that are not present in the original dataset (for example, _RARE_
). These categories appear as a means of rare category protection, ensuring privacy protection of subjects with rare or unique features.
Technical reference
All variables are binned into a maximum of 10 groups. For categorical columns, the 10 most common categories are used and for numerical and datetime columns, the deciles are chosen as cutoffs. One additional group is used to show empty values: (empty) for categorical and (n/a) for numerical and datetime columns.
In the downloadable HTML, only a selection of bivariate plots are shown, half of which are the most accurate and the other half the least accurate.
Accuracy
The Accuracy section is a summary of all Univariate and Bivariate distributions. It displays a table with all variables, the second column and third column of which list the respective univariate and bivariate accuracies.
At the bottom is an Accuracy Matrix that shows the bivariate accuracies between pairs of variables. You can also find these values in the respective Bivariate distribution charts. The diagonal values (bottom left to top right) of the chart that only belong to one variable are the univariate accuracies of that variable.
How accuracy is calculated
The accuracy of synthetic data can be assessed by measuring statistical distances between the synthetic and the original data. The metric of choice for the statistical distance is the total variation distance (TVD), which is calculated for the discretized empirical distributions. Subtracting the TVD from 100% then yields the reported accuracy measure. These are being calculated for all univariate and all bivariate distributions. The latter is done for all pairwise combinations within the original data, as well as between the context and the target. For sequential data, an additional coherence metric is calculated that assesses the bivariate accuracy between the value of a column, and the succeeding value of a column. All of these individuallevel statistics are then averaged across to provide a single informative quantitative measure. The full list of calculated accuracies is provided as a separate downloadable file.
Coherence
Coherence charts compare original and synthetic timeseries data and their inherent correlations. A coherence chart is a bivariate plot of a column of data. The data from the column is copied into two separate columns, where the first column contains the sequence of earlier events and the second columns contains the sequence of later events. You can use coherence charts to check the probability of one type of event being followed by another.
Coherence charts are available only for linked tables. You can review the coherence of linked tables on the Coherence section of the Model report and Data report.
To interpret coherence charts, you can review the following example where a color with a higher saturation indicates higher probability that an event from a Yaxis event is followed by an Xaxis event.
Distances
The full distribution of the distances to closest records appears at the end of the Model report.
The distances of the synthetic samples to the training samples are displayed in green, and the distances of the synthetic samples to the holdout samples are displayed in gray.
In the chart, you can ascertain that the synthetic samples are as close to the original training samples as they are to the holdout samples (which serves as a reference).