Data Quality

Data quality
Data quality monitors enable teams to quickly detect when features, predictions, or actual data points don't conform to what is expected.

Superwise offers you a number of Data quality monitoring policy templates:

Missing Values

You can monitor for missing values at the feature level for any segment.

Metric	Role	Segments
missing values	All Features role	Entire set

Out-of-Range

You can monitor for anomalies as a percent of outliers at the feature level (numeric only).

Metric	Role	Segments
outliers	All Numeric Features	Entire set

New Values

Superwise can also monitor anomalies as a percent of new values at the feature level (categorical only).

Metric	Role	Segments
new values	All Categorical Features	Entire set

Fixed Value

detect features that are constant on the entire set level

Metric	Role	Segments
std / entropy / prop	All Features role	Entire set