The quality report captures the Column Shapes, Column Pair Trends and Table Relationships. This guide contains some technical details about each property.
Does the synthetic data capture the shape of each column?
The shape of a column describes its overall distribution. The higher the score, the more similar the distributions of real and synthetic data.
This property applies metrics based on the column types.
This yields a separate score for every column. The final Column Shapes score is the average of all columns.
You may notice that column shape quality is better for discrete columns (categorical, boolean) as opposed to continuous columns (numerical, datetime). Generally, we've found that it's much easier to create synthetic data for a small number of known categories than large ranges of numerical values.
Does the synthetic data capture trends between pairs of columns?
The trend between two columns describes how they vary in relation to each other, for example the correlation. The higher the score, the more the trends are alike.
This property applies a different metric metric based on the type of data
numerical (or datetime) with another numerical (or datetime)
categorical (or boolean) with another categorical (or boolean)
numerical (or datetime) with a categorical (or boolean)
This yields a score between every pair of columns. The Column Pair Trends score is the average of all the scores.
The CorrelationSimilarity metric works by computing a separate value for the real vs. the synthetic data. The Quality Report shows a side-by-side visualization for real vs. synthetic data when applicable.
This property is only available for multi table datasets.
Does the synthetic data capture the number of connections between parent and child tables? This is also known as the cardinality of the tables.
This property applies the CardinalityShapeSimilarity metric for every set of connected tables: parent table and child table.
Higher order distributions of 3 or more columns are not included in the Quality Report. We have found that very high order similarity may have an adverse effect on the synthetic data usability; after a certain point, it indicates that the synthetic data is just a copy of the real data. (For more information, see the NewRowSynthesis metric.)