How does explainability work?

Kumo Engine generates explanation files after fitting a model, when the task and model support them:

• Gradient-based column scores summarize sensitivity to input features by table and column.
• Column cohorts group feature values into ranges or categories and report population shares, average predictions, and observed targets when available.
• Sampled graph context records nodes, relationships, and feature values for selected examples.

These describe the model on the test examples analyzed. They do not establish causality or explain every later prediction.

Trainer.fit() requests explanations unless trainer.model_plan.training_job.disable_explain is enabled. Files are written under the selected experiment, or its training-and-validation refit when present. Save them outside scratch if you need to retain them; they are not included in a published model bundle.

Classification and regression are eligible for all three methods. Link prediction supports gradient scores and sampled graph context; forecasting supports gradient scores. Multilabel ranking is not supported by these methods.

Missing Explanation Scores

A missing score does not mean the column was excluded from training. Check task and model support, disable_explain, and fit logs. An explanation step can fail without failing the fit.

Inspect the selected model’s encoder settings to confirm feature inclusion. A Null() encoder excludes a column from model features but preserves its key relationships in the graph. See Encoder Selection.

See Model Explainability for the support matrix and code to locate and read the files.