Results
Retrieve prediction outputs and the raw explanation data behind them.
The Results view is the primary deployed model view for querying predictions and retrieving the raw explanation data behind them.
Use it when you need the predicted output together with the raw dataframes that support prediction-level explanations, including feature attributions, module attributions, partition activations, and partition attributions.
When to use
| Use case | Description |
|---|---|
| Run inference | Retrieve the predicted output for one or more query rows. |
| Inspect raw explanation data | Get the underlying attribution and activation data used by other explanation views. |
| Debug a prediction | Compare the query input, prediction, total attribution, and attribution breakdowns in one response. |
| Build custom visualisations | Use the returned dataframes directly in tables, charts, or downstream analysis. |
Generate the view
Generate the Results view from a deployed model.
curl -X POST https://api.hi.umnai.com/deployed-models/deployedModelId/views \-H "X-Views-Cache-Control: no-cache, no-store" \-H "Authorization: Bearer <token>" \-H "Content-Type: application/json" \-d '{"data": [{"view_type": "RESULTS"}],"query": {"age": [58],"capital_45_gain": [0],"capital_45_loss": [0],"education": ["HS-grad"],"educational_45_num": [9],"fnlwgt": [299831],"gender": ["Male"],"hours_45_per_45_week": [35],"marital_45_status": ["Married-civ-spouse"],"native_45_country": ["United-States"],"occupation": ["?"],"race": ["White"],"relationship": ["Husband"],"workclass": ["?"]},"targets": {"income": [0]}}'
Response
{"data": [{"view_type": "RESULTS","commons_version": {"major_version": 0,"minor_version": 18,"patch_version": 0,"build_version": "dev72"},"core_version": {"major_version": 7,"minor_version": 10,"patch_version": 0,"build_version": "dev52"},"output_version": {"major_version": 2,"minor_version": 0,"patch_version": 0},"view_data": {"model_info": {"columns": ["model_intercept","model_id","dataset_name","commons_version","core_version","estimators_aggregation_type","estimators_aggregation_weights","estimators_aggregation_bias"],"index": [0],"data": [[-1.0836982274,"2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533","2bcb9dc8-adult_income-root","0.18.0.dev72","7.10.0.dev52","MEAN",null,[0]]],"foreign_keys": [],"labels": [],"types": [{"data_type": "NUMBER","format": "FLOAT64"},{"data_type": "STRING","format": null},{"data_type": "STRING","format": null},{"data_type": "STRING","format": null},{"data_type": "STRING","format": null},{"data_type": "STRING","format": null},{"data_type": 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The request needs view_type set to RESULTS and query data aligned with the deployed model input schema.
Output
The response follows the shared Response structure format.
The Results view returns multiple named dataframes inside view_data.
| Dataframe | Description |
|---|---|
model_info | Metadata about the model and the inference output, including model identifier and intercept information. |
query_input | The query values submitted to the deployed model, organised by input feature. |
feature_attribution | Attribution values organised by input feature. |
total_attribution | Total attribution for each query row. |
predicted_output | Predicted output for each query row. |
module_attribution | Attribution values organised by model module. |
partition_activation | Partition activation values for each query row. |
partition_attribution | Attribution values organised by partition. |
decomposition_activation | Activation values for decomposition components. |
decomposition_attribution | Attribution values for decomposition components. |
estimators_predicted_output | Optional predicted output from each estimator, when estimator output is available. |
estimators_feature_attribution | Optional feature attribution values by estimator. |
estimators_module_attribution | Optional module attribution values by estimator. |
estimators_partition_attribution | Optional partition attribution values by estimator. |
estimators_decomposition_attribution | Optional decomposition attribution values by estimator. |
The Results view also includes commons_version and core_version metadata alongside the common view metadata.
Interpret the result
The Results view is easiest to read from the prediction outward: start with the predicted output, then inspect attribution, modules, partitions, and estimator data when available.
Start with the prediction
Use predicted_output as the model response for each query row.
For a single query row, the dataframe contains the prediction returned by the deployed model. For a batch query, each row corresponds to a query row.
Use query_input beside predicted_output when you need to display the request and result together.
Check total attribution
Use total_attribution to understand the total contribution behind the prediction.
For many models, the prediction can be read as the model intercept plus the total attribution. The model_info dataframe contains model-level information such as the intercept and model identifier.
This is useful when you need to check whether the explanation data aligns with the prediction output.
Inspect feature and module contributions
Use feature_attribution to see the contribution of each input feature.
Use module_attribution to see the contribution of each module. A module may represent a single feature or an interaction between features, so module attribution can be more structural than feature attribution.
If you need a cleaner, user-facing rollup of these values, use Feature attribution or Module attribution.
Trace partitions and decompositions
Use partition_activation to see which partitions were active for the query.
Use partition_attribution to see how much each partition contributed. Then use decomposition_activation and decomposition_attribution when you need a finer-grained breakdown of the activated model components.
These dataframes are useful for debugging or building custom explanation tools, but they are lower-level than the attribution views.
Compare estimator outputs
If estimator data is present, use the estimators_* dataframes to inspect how individual estimators contributed.
Large differences across estimator outputs or attributions can indicate uncertainty or instability. For a user-facing view of strength, uncertainty, and adjusted error, use Strengths and weaknesses.
Field reference
The Results view returns dataframes, so individual columns are not documented as standalone API schema properties. Many columns are dynamic and depend on the model features, targets, modules, partitions, decompositions, and estimators.
model_info
The model_info dataframe contains model-level metadata for the result.
| Field | Description |
|---|---|
model_intercept | Model intercept term used in the prediction. |
model_id | ID of the model behind the deployed model. |
dataset_name | Name of the dataset used to train the model, when available. |
commons_version | Version of the commons library used to generate the result, when included in the dataframe. |
core_version | Version of the core library used to generate the result, when included in the dataframe. |
estimators_aggregation_type | Method used to aggregate estimator outputs, when available. |
estimators_aggregation_weights | Weights applied to estimator outputs, when available. |
estimators_aggregation_bias | Bias term applied during estimator aggregation, when available. |
query_input
The query_input dataframe contains the input values submitted to the deployed model.
| Column pattern | Description |
|---|---|
<input_feature> | Value of the input feature for each query row. |
feature_attribution
The feature_attribution dataframe contains attribution values by input feature.
| Column pattern | Description |
|---|---|
<input_feature> | Attribution value for the input feature. Positive values raise the prediction; negative values lower it. |
total_attribution
The total_attribution dataframe contains the total attribution for each query row.
| Column pattern | Description |
|---|---|
<target_index> | Total attribution for the target output. |
predicted_output
The predicted_output dataframe contains the model prediction for each query row.
| Column pattern | Description |
|---|---|
<target_index> | Predicted value for the target output. |
module_attribution
The module_attribution dataframe contains attribution values by model module.
| Column pattern | Description |
|---|---|
<module_index> | Attribution value for the module. |
partition_activation
The partition_activation dataframe indicates which partitions were activated.
| Column pattern | Description |
|---|---|
<partition_index> | Activation value for the partition. |
partition_attribution
The partition_attribution dataframe contains attribution values by partition.
| Column pattern | Description |
|---|---|
<partition_index> | Attribution value for the partition. |
decomposition_activation
The decomposition_activation dataframe indicates which decomposition components were activated.
| Column pattern | Description |
|---|---|
<decomposition_index> | Activation value for the decomposition component. |
decomposition_attribution
The decomposition_attribution dataframe contains attribution values by decomposition component.
| Column pattern | Description |
|---|---|
<decomposition_index> | Attribution value for the decomposition component. |
Estimator dataframes
Estimator dataframes are included when estimator output is available.
| Dataframe | Description |
|---|---|
estimators_predicted_output | Predicted output from each estimator. |
estimators_feature_attribution | Feature attribution values by estimator. |
estimators_module_attribution | Module attribution values by estimator. |
estimators_partition_attribution | Partition attribution values by estimator. |
estimators_decomposition_attribution | Decomposition attribution values by estimator. |