Explanation views | Umnai

Explanation Lab provides local views of a prediction, global views of learned model structure and rules, and tools for generating written interpretations. These guides use Adult Income examples to show how to query a record, inspect its contributions and review the model’s explanations.

What a view explains

A view presents information derived from a data resource, a trained model or a deployed model. The resource determines what can be inspected: data views show lineage and distributions, model views describe learned structure, and deployed-model views provide predictions, explanations and evaluation results.

In the web app, these outputs appear in the area where you use them. Inspect data in Data Lab, evaluate a running deployment in Model Lab, and explore predictions and learned behaviour in Explanation Lab. See the Data Lab overview and Model Lab overview for those related workflows.

Prediction, attribution and model behaviour

A prediction is the output produced for a set of input values. Feature attributions describe how the inputs contributed to that output; module attributions relate it to the model’s learned modules, partitions and rules.

A local explanation concerns one query. Global views explore learned structure and behaviour across feature values. A decision rule describes a learned condition; a dependency plot helps show how a module behaves as its inputs vary. These views answer different questions and should be read together when investigating a result.

Evaluation measures performance across a data population. It is separate from explaining an individual prediction: use Evaluate models for classification or regression metrics, and retain the deployment and data selection when comparing results.

Choose an explanation view

Start with a deployed model and decide what you need to understand. Local views explain a particular set of inputs; global views expose the model’s structure and module behavior.

View	Use it to
Local Explanations	Execute an individual query and inspect feature and module attributions.
Global Explanations	Browse model structure, module dependencies, and decision rules.
Dependency Graph	Trace the displayed dependencies from inputs through modules to the output.
Module Explorer	Open the Module Dependency Plot Explorer and adjust its display controls.
XNN LLM Explanations	Generate a written interpretation of the built-in Adult Income sample data.
Symbolic Explanations	Generate a written interpretation of a selected module’s decision rules.

Before you begin

Confirm that the intended deployment is RUNNING, check the meaning of its input fields and prepare the record you want to explain. Use a record that is relevant to the task you are investigating.

The examples include zero values and special category labels. Check the input conventions for your model before submitting a query. A displayed default is not necessarily a suitable input for your record.

Follow a question through the views

For one decision, run a local query and inspect its feature and module attributions.
For a contributing module, inspect its global decision rules and dependency plot.
Use a dependency graph to trace the displayed computation for the same record.
If you need written text, review a symbolic explanation against the source rules and retain the context needed to reproduce your interpretation.

The XNN-assisted LLM sample workflow is separate: it interprets supplied sample data and does not inherit the deployed model selected in another view.

Next steps

Continue to Local explanations.