> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.umnai.com/guides/views/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.umnai.com/_mcp/server.

# Views

## Docs

- [Overview](https://docs.umnai.com/guides/views/overview.md): Generate views to inspect data, models, predictions, and explanations.
- [Response structure](https://docs.umnai.com/guides/views/response-structure.md): Understand the common shape of view responses.
- [Data views](https://docs.umnai.com/guides/views/data-views/overview.md): Inspect lineage and distribution for datasources and datasets.
- [Datasource lineage](https://docs.umnai.com/guides/views/data-views/datasource-lineage.md): Trace how a datasource was created from raw inputs.
- [Dataset lineage](https://docs.umnai.com/guides/views/data-views/dataset-lineage.md): Trace how a dataset was created from its datasource and raw inputs.
- [Dataset histogram](https://docs.umnai.com/guides/views/data-views/dataset-histogram.md): Inspect feature and target distributions in an onboarded dataset.
- [Model views](https://docs.umnai.com/guides/views/model-views/overview.md): Explore trained model structure and comparisons.
- [Structure](https://docs.umnai.com/guides/views/model-views/structure.md): Inspect modules, partitions, rules, bins, and model metadata.
- [Comparison](https://docs.umnai.com/guides/views/model-views/comparison.md): Compare the structure of two trained models.
- [Deployed model views](https://docs.umnai.com/guides/views/deployed-model-views/overview.md): Query deployed models and inspect prediction-level outputs.
- [Results](https://docs.umnai.com/guides/views/deployed-model-views/results.md): Retrieve prediction outputs and the raw explanation data behind them.
- [Feature attribution](https://docs.umnai.com/guides/views/deployed-model-views/feature-attribution.md): Understand how input features contributed to a prediction.
- [Module attribution](https://docs.umnai.com/guides/views/deployed-model-views/module-attribution.md): Understand how model modules and activated rules contributed to a prediction.
- [Decision actions](https://docs.umnai.com/guides/views/deployed-model-views/decision-actions.md): Inspect partition-level activations, attributions, and rule expressions.
- [Module dependency](https://docs.umnai.com/guides/views/deployed-model-views/module-dependency.md): Inspect how selected module features affect attribution across sampled values.
- [Anomalies](https://docs.umnai.com/guides/views/deployed-model-views/anomalies.md): Identify anomalous feature bins in deployed model data.
- [Strengths and weaknesses](https://docs.umnai.com/guides/views/deployed-model-views/strengths-and-weaknesses.md): Estimate prediction strength, uncertainty, and adjusted error.
- [Classification evaluation](https://docs.umnai.com/guides/views/deployed-model-views/classification-evaluation.md): Evaluate binary classification performance across thresholds.
- [Regression evaluation](https://docs.umnai.com/guides/views/deployed-model-views/regression-evaluation.md): Evaluate regression performance with error and fit metrics.