Ctrlk
HomeBlogNannyML OSS Docs
For the complete documentation index, see llms.txt. This page is also available as Markdown.

Data quality

The data quality dashboard allows for analyzing the changes in missing and unseen values over time. Here is our guide explaining how to use the data quality dashboard:

Data quality dashboard.

There are three main components of the Data quality dashboard:

1. Filters

Filter which data quality metrics you want to see. Data quality metrics that are not calculated are not visible under the filter. Selecting which data quality metrics you want to calculate can be done under model settings.

Note that unseen values do not apply to continuous columns. It is typically used to assess if new categories are appearing on which the model was not trained.

Metrics filter.

Filter the resulting charts by the columns you want to see.

Note that the data quality metrics can also be applied to the model output and the target, not just the features.

Columns filter.

Select which metrics to display depending on whether there are alerts in the last chunk, alerts in the previous chunks, or no alerts at all, or include all charts regardless of when and if any alerts occurred.

Alert status filter.

Filter charts by the previously specified tags.

Tags filter.

2. Visualisations

You can change the order of charts based on the metric name, number, or recency of the alerts.

Sort by window.

You can select a specific period of interest which applies to all charts.

Date range window.

To reset a previously set date period, whether using the date range or slider, simply press the "Reset" button.

Date range reset.

Similar to selecting a date range, you can choose a specific period of interest by simply moving the date slider.

Date slider

The charts are interactive; you can hover over them for more details. Red squares signal alerts, and the blue line represents the metric in the reference period. The light blue line indicates the metric during the analysis period.

You can also zoom in on any part of a chart. Simply press and hold your mouse button, then draw a square over your area of interest. To reset the zoom, just double-click on the chart.

Data quality plot.

3. Plots

There are two types of plot formats: line and step. A line plot smoothly connects points with straight lines to show trends, while a step plot uses sharp vertical and horizontal lines to show exact changes between points clearly.

Plot formats.

Select datasets to zoom in on reference, analysis, or create a separate subplot for both.

Datasets plots.

Toggle on or off some components on the charts, like alerts, confidence bands, thresholds, and legends.

Plot elements.
PreviousCovariate shift
NextLogs

Last updated