Comparison

The Comparison view compares two trained models.

Use it to understand how a baseline model differs from another trained model, especially after changing training data, schema settings, training configuration, constraints, or model version.

When to use

Generate the view

Generate the Comparison view from the baseline model.

The model in the path is treated as the baseline model. The request body must include comparison_model_id, which identifies the model to compare against the baseline.

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{
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  "data": [
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    {
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      "view_type": "MODEL_COMPARISON",
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      "comparison_model_id": "comparison-model-id"
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    }
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  ]
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}

Output

The response follows the shared Response structure format.

The Comparison view returns multiple named dataframes inside view_data.

Interpret the result

The Comparison view is easiest to read from broad differences to detailed differences: start with summary, then inspect changed modules, then review changed partitions.

Start with the summary

Use summary to understand which high-level model attributes changed.

The attributes column identifies the attribute being compared. baseline_model and comparison_model show the values on each side. delta shows the change, and edit_operation describes whether the attribute was added, deleted, or edited.

This is the fastest way to see whether the two models differ in overall structure before inspecting the detailed dataframes.

Inspect module changes

Use modules to understand how the model organisation changed.

The module_operation columns identify the module and whether it was added, deleted, or edited. The baseline_model and comparison_model columns show the module identifiers, indexes, partition counts, parameter counts, and non-zero parameter counts on each side. The delta columns show the size of the change.

Focus first on added or deleted modules, then inspect edited modules with large changes in delta.n_partitions, delta.n_params, or delta.non_zero_params.

Review partition changes

Use partitions to inspect changes inside modules.

The partition_operation columns identify the rule and edit operation. The baseline_partition and comparison_partition columns show the module, rule, intercept, coefficients, and transformed features on each side. The delta columns show the changes to the rule expression components.

Partition changes are useful when the model appears similar at the module level but behaves differently because the rules inside a module changed.

Compare models before deployment

Use the Comparison view before deployment when you need to choose between two trained models or understand the impact of a training change.

For prediction-level differences, deploy the relevant model and use deployed model views to compare predictions, attributions, diagnostics, or evaluation results.

Field reference

The Comparison view returns dataframes, so individual columns are not documented as standalone API schema properties. This section describes the main columns returned in each dataframe.

summary

The summary dataframe contains high-level differences between the baseline model and the comparison model.

modules

The modules dataframe contains module-level differences between the two models.

partitions

The partitions dataframe contains partition-level differences between the two models.