ViewsDeployed model views

Decision actions

Inspect partition-level activations, attributions, and rule expressions.

View as Markdown

The Decision actions view returns a partition-level table for a deployed model query.

Use it when you need to inspect which model partitions were activated, how much they contributed, and which rule conditions, THEN expressions, bins, and decomposition values were involved.

When to use

Use caseDescription
Inspect activated rulesSee which partitions activated for a query or batch query.
Debug a predictionReview attribution, activation, condition, and THEN-expression data at partition level.
Compare query behaviour to training dataUse comparison mode to compare query activations and attributions against train, test, or all observations.
Build advanced explanation toolingUse the partition-level table as a lower-level data source for custom views, diagnostics, or dashboards.

Generate the view

Generate the Decision actions view from a deployed model.

POST
/deployed-models/:deployedModelId/views
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": "DECISION_ACTION_TABLE"
    }
  ],
  "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": "DECISION_ACTION_TABLE",
      "justification_data": {
        "columns": [
          "decomposition_index",
          "module_id",
          "module_index",
          "module_name",
          "module_features",
          "dataset_id",
          "rule_id",
          "module_partition_index",
          "global_partition_index",
          "condition_expr_string",
          "decomposed_then_expr_string",
          "decomposed_then_expr_object",
          "then_expr_string",
          "condition_expr_object",
          "then_expr_object",
          "bin_id",
          "condition_expr_friendly",
          "condition_expr_json",
          "output_target_index",
          "output_target_name",
          "input_feature",
          "low",
          "high",
          "low_inclusive",
          "high_inclusive",
          "is_categorical",
          "category_value",
          "bin_description",
          "n_module_features",
          "module_feature_index",
          "feature_participation",
          "decomposition_id",
          "feature_bin_index",
          "created_time",
          "model_id",
          "attribution",
          "activations",
          "attribution_bin_def",
          "activation_bin_def"
        ],
        "index": [
          0,
          1,
          2,
          3,
          4,
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          24,
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          73,
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          99,
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          132,
          150,
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          172,
          187,
          189,
          195,
          197
        ],
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            "-1373.49132072808*native_45_country[Yugoslavia] - 1373.81964960444*native_45_country[Vietnam] - 1373.57707575913*native_45_country[United-States] - 1373.84561399215*native_45_country[Trinadad&Tobago] - 1373.64872942854*native_45_country[Thailand] - 1373.46703915566*native_45_country[Taiwan] - 1373.68412824989*native_45_country[South] - 1373.68340264088*native_45_country[Scotland] - 1373.73908978601*native_45_country[Puerto-Rico] - 1373.65835485868*native_45_country[Portugal] - 1373.66729774112*native_45_country[Poland] - 1373.51928244595*native_45_country[Philippines] - 1373.76001353958*native_45_country[Peru] - 1373.8982031345*native_45_country[Outlying-US(Guam-USVI-etc)] - 1373.81711339109*native_45_country[Nicaragua] - 1373.81628628932*native_45_country[Mexico] - 1373.85927706226*native_45_country[Laos] - 1373.39428115886*native_45_country[Japan] - 1373.67690880471*native_45_country[Jamaica] - 1373.41193795971*native_45_country[Italy] - 1373.51059881189*native_45_country[Ireland] - 1373.51836905039*native_45_country[Iran] - 1373.4501643481*native_45_country[India] - 1373.47855723968*native_45_country[Hungary] - 1373.59594318578*native_45_country[Hong] - 1373.7847636973*native_45_country[Honduras] - 1373.66244797815*native_45_country[Haiti] - 1373.8437496856*native_45_country[Guatemala] - 1373.64088036516*native_45_country[Greece] - 1373.51722533671*native_45_country[Germany] - 1373.24442037211*native_45_country[France] - 1373.34715278159*native_45_country[England] - 1373.81005518408*native_45_country[El-Salvador] - 1373.76279567794*native_45_country[Ecuador] - 1373.79750407635*native_45_country[Dominican-Republic] - 1373.59672134582*native_45_country[Cuba] - 1373.81706916958*native_45_country[Columbia] - 1373.49153224177*native_45_country[China] - 1373.42566687299*native_45_country[Canada] - 1373.47021912432*native_45_country[Cambodia] - 1373.58743904684*native_45_country[?] - 1373.90391305672*native_45_country[__INFREQUENT__] + 1373.57063082812 - 1373.68542560183*native_45_country[__MASK__]",
            "null",
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            "-1407.73028126957*marital_45_status[Widowed] - 1407.74589599637*marital_45_status[Separated] - 1407.7747355444*marital_45_status[Never-married] - 1407.73707086858*marital_45_status[Married-spouse-absent] - 1407.60428096682*marital_45_status[Married-civ-spouse] - 1407.58005107107*marital_45_status[Married-AF-spouse] - 1407.75977090328*marital_45_status[Divorced] - 1407.70615311252*marital_45_status[__MASK__] + 322.311546022997*education[Some-college] + 322.49389208042*education[Prof-school] + 322.167214836282*education[Preschool] + 322.430307294961*education[Masters] + 322.271697205032*education[HS-grad] + 322.449813807926*education[Doctorate] + 322.387946978595*education[Bachelors] + 322.301679649577*education[Assoc-voc] + 322.312240190316*education[Assoc-acdm] + 322.196861449532*education[9th] + 322.185567461496*education[7th-8th] + 322.203810137829*education[5th-6th] + 322.191546108185*education[1st-4th] + 322.266330240924*education[12th] + 322.256621802012*education[11th] + 322.231838992133*education[10th] + 322.295639513266*education[__MASK__] + 1085.35660006293",
            "null",
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          "['occupation', 'Sales', 'activations']",
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          "['occupation', '__MASK__', 'activations']",
          "['race', 'Amer-Indian-Eskimo', 'activations']",
          "['race', 'Asian-Pac-Islander', 'activations']",
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          "['race', 'Other', 'activations']",
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          "['race', '__MASK__', 'activations']",
          "['relationship', 'Husband', 'activations']",
          "['relationship', 'Not-in-family', 'activations']",
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          "['relationship', '__MASK__', 'activations']",
          "['workclass', '?', 'activations']",
          "['workclass', 'Federal-gov', 'activations']",
          "['workclass', 'Local-gov', 'activations']",
          "['workclass', 'Never-worked', 'activations']",
          "['workclass', 'Private', 'activations']",
          "['workclass', 'Self-emp-inc', 'activations']",
          "['workclass', 'Self-emp-not-inc', 'activations']",
          "['workclass', 'State-gov', 'activations']",
          "['workclass', 'Without-pay', 'activations']",
          "['workclass', '__MASK__', 'activations']",
          "['age', ['GT', -inf, 'LT', inf], 'attribution']",
          "['capital_45_gain', ['GT', -inf, 'LT', inf], 'attribution']",
          "['capital_45_loss', ['GT', -inf, 'LT', inf], 'attribution']",
          "['education', '10th', 'attribution']",
          "['education', '11th', 'attribution']",
          "['education', '12th', 'attribution']",
          "['education', '1st-4th', 'attribution']",
          "['education', '5th-6th', 'attribution']",
          "['education', '7th-8th', 'attribution']",
          "['education', '9th', 'attribution']",
          "['education', 'Assoc-acdm', 'attribution']",
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          "['education', 'Masters', 'attribution']",
          "['education', 'Preschool', 'attribution']",
          "['education', 'Prof-school', 'attribution']",
          "['education', 'Some-college', 'attribution']",
          "['education', '__MASK__', 'attribution']",
          "['educational_45_num', ['GT', -inf, 'LT', inf], 'attribution']",
          "['gender', 'Female', 'attribution']",
          "['gender', 'Male', 'attribution']",
          "['gender', '__MASK__', 'attribution']",
          "['hours_45_per_45_week', ['GT', -inf, 'LT', inf], 'attribution']",
          "['marital_45_status', 'Divorced', 'attribution']",
          "['marital_45_status', 'Married-AF-spouse', 'attribution']",
          "['marital_45_status', 'Married-civ-spouse', 'attribution']",
          "['marital_45_status', 'Married-spouse-absent', 'attribution']",
          "['marital_45_status', 'Never-married', 'attribution']",
          "['marital_45_status', 'Separated', 'attribution']",
          "['marital_45_status', 'Widowed', 'attribution']",
          "['marital_45_status', '__MASK__', 'attribution']",
          "['native_45_country', '?', 'attribution']",
          "['native_45_country', 'Cambodia', 'attribution']",
          "['native_45_country', 'Canada', 'attribution']",
          "['native_45_country', 'China', 'attribution']",
          "['native_45_country', 'Columbia', 'attribution']",
          "['native_45_country', 'Cuba', 'attribution']",
          "['native_45_country', 'Dominican-Republic', 'attribution']",
          "['native_45_country', 'Ecuador', 'attribution']",
          "['native_45_country', 'El-Salvador', 'attribution']",
          "['native_45_country', 'England', 'attribution']",
          "['native_45_country', 'France', 'attribution']",
          "['native_45_country', 'Germany', 'attribution']",
          "['native_45_country', 'Greece', 'attribution']",
          "['native_45_country', 'Guatemala', 'attribution']",
          "['native_45_country', 'Haiti', 'attribution']",
          "['native_45_country', 'Honduras', 'attribution']",
          "['native_45_country', 'Hong', 'attribution']",
          "['native_45_country', 'Hungary', 'attribution']",
          "['native_45_country', 'India', 'attribution']",
          "['native_45_country', 'Iran', 'attribution']",
          "['native_45_country', 'Ireland', 'attribution']",
          "['native_45_country', 'Italy', 'attribution']",
          "['native_45_country', 'Jamaica', 'attribution']",
          "['native_45_country', 'Japan', 'attribution']",
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          "['native_45_country', 'Outlying-US(Guam-USVI-etc)', 'attribution']",
          "['native_45_country', 'Peru', 'attribution']",
          "['native_45_country', 'Philippines', 'attribution']",
          "['native_45_country', 'Poland', 'attribution']",
          "['native_45_country', 'Portugal', 'attribution']",
          "['native_45_country', 'Puerto-Rico', 'attribution']",
          "['native_45_country', 'Scotland', 'attribution']",
          "['native_45_country', 'South', 'attribution']",
          "['native_45_country', 'Taiwan', 'attribution']",
          "['native_45_country', 'Thailand', 'attribution']",
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          "['occupation', '?', 'attribution']",
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          "['occupation', '__MASK__', 'attribution']",
          "['race', 'Amer-Indian-Eskimo', 'attribution']",
          "['race', 'Asian-Pac-Islander', 'attribution']",
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          "['race', 'Other', 'attribution']",
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          "['race', '__MASK__', 'attribution']",
          "['relationship', 'Husband', 'attribution']",
          "['relationship', 'Not-in-family', 'attribution']",
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          "['relationship', 'Own-child', 'attribution']",
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          "['relationship', '__MASK__', 'attribution']",
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          "['workclass', 'Never-worked', 'attribution']",
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          "decomposition_equation(age)",
          "decomposition_equation(capital_45_gain)",
          "decomposition_equation(capital_45_loss)",
          "decomposition_equation(education)",
          "decomposition_equation(educational_45_num)",
          "decomposition_equation(gender)",
          "decomposition_equation(hours_45_per_45_week)",
          "decomposition_equation(marital_45_status)",
          "decomposition_equation(native_45_country)",
          "decomposition_equation(occupation)",
          "decomposition_equation(race)",
          "decomposition_equation(relationship)",
          "decomposition_equation(workclass)",
          "decomposition_value(age)",
          "decomposition_value(capital_45_gain)",
          "decomposition_value(capital_45_loss)",
          "decomposition_value(education)",
          "decomposition_value(educational_45_num)",
          "decomposition_value(gender)",
          "decomposition_value(hours_45_per_45_week)",
          "decomposition_value(marital_45_status)",
          "decomposition_value(native_45_country)",
          "decomposition_value(occupation)",
          "decomposition_value(race)",
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2812.02942549768*occupation[?] - 2811.98294839324*occupation[Adm-clerical] - 2811.76391665603*occupation[Armed-Forces] - 2812.01914489524*occupation[Craft-repair] - 2811.84162029987*occupation[Exec-managerial] - 2812.13081568926*occupation[Farming-fishing] - 2812.05727008911*occupation[Handlers-cleaners] - 2812.06111199579*occupation[Machine-op-inspct] - 2812.04064542925*occupation[Other-service] - 2812.03442896919*occupation[Priv-house-serv] - 2811.88051767519*occupation[Prof-specialty] - 2811.941975621*occupation[Protective-serv] - 2811.94536611623*occupation[Sales] - 2811.90639170157*occupation[Tech-support] - 2812.04532974366*occupation[Transport-moving] - 2811.98056735817*occupation[__MASK__])', 169: '382.518894139559*marital_45_status[Divorced] + 382.766829125974*marital_45_status[Married-AF-spouse] + 382.722630283971*marital_45_status[Married-civ-spouse] + 382.552088023204*marital_45_status[Married-spouse-absent] + 382.509588251914*marital_45_status[Never-married] + 382.530430285326*marital_45_status[Separated] + 382.545255047802*marital_45_status[Widowed] + 382.596479385451*marital_45_status[__MASK__] + 2429.32994086609*(382.518894139559*marital_45_status[Divorced] + 382.766829125974*marital_45_status[Married-AF-spouse] + 382.722630283971*marital_45_status[Married-civ-spouse] + 382.552088023204*marital_45_status[Married-spouse-absent] + 382.509588251914*marital_45_status[Never-married] + 382.530430285326*marital_45_status[Separated] + 382.545255047802*marital_45_status[Widowed] + 382.596479385451*marital_45_status[__MASK__])/(382.518894139559*marital_45_status[Divorced] + 382.766829125974*marital_45_status[Married-AF-spouse] + 382.722630283971*marital_45_status[Married-civ-spouse] + 382.552088023204*marital_45_status[Married-spouse-absent] + 382.509588251914*marital_45_status[Never-married] + 382.530430285326*marital_45_status[Separated] + 382.545255047802*marital_45_status[Widowed] + 382.596479385451*marital_45_status[__MASK__] - 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            "nan",
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The request needs view_type set to DECISION_ACTION_TABLE and query data aligned with the deployed model input schema.

Output

The response follows the shared Response structure format.

The Decision actions view returns two dataframes.

DataframeDescription
view_dataThe decision action table. Each row represents a model partition and includes activation counts, attribution statistics, rule expressions, and dynamic bin or decomposition columns.
justification_dataSupporting provenance data used to trace the decision action rows back to lower-level model structures.

This view is primarily intended for programmatic consumption. It can return many dynamic columns, especially when expanded is enabled or when the model has many categorical bins.

Interpret the result

The Decision actions view is easiest to read by finding the most active or impactful partitions first, then using the expression and bin columns to understand why those partitions mattered.

Start with partition identity

Use global_partition_index, module_index, and module_name to identify the partition and the module it belongs to.

These columns let you connect a decision action row back to the model structure. global_partition_index identifies the partition across the whole model, while module_index and module_name identify the module that owns it.

Review activation counts

Use activations to see how often the partition activated for the query rows.

Use impactful_activations to focus on activations whose attribution exceeded the configured impactful_threshold. This helps separate partitions that fired from partitions that had meaningful contribution.

Inspect attribution strength

Use absolute_attribution_sum and absolute_attribution_mean to find the partitions with the largest overall impact.

Use attribution_sum and attribution_mean when you care about direction. Positive values increase the prediction; negative values decrease it.

For batch queries, the min, max, and quantile fields help you understand whether a partition behaves consistently across rows or only matters for a subset of rows.

Read rule expressions

Use condition_expr_string to understand when a partition applies.

Use then_expr_strings to understand the expression contributed when the partition is active. Together, these fields describe the rule-like behaviour represented by the partition.

When expanded is enabled, the condition expression is also expanded into additional columns for feature bounds and categorical conditions.

Inspect dynamic bin and decomposition columns

The Decision actions table can include dynamic columns for bin-level activations and attributions.

For continuous features, dynamic column names identify the feature, numeric bin range, and metric. For categorical features, they identify the feature, category value, and metric.

These columns are useful when you need to inspect how a partition’s activation or attribution decomposes across feature bins.

Use comparison mode when needed

Set observation_type to compare the query rows against a baseline from TRAIN, TEST, or ALL data.

In comparison mode, activation and attribution metrics are returned for the baseline, the query comparison, and the delta between them. This is useful when you want to understand how a query, batch, or segment differs from the model’s training or test behaviour.

Modes

Query mode

Query mode is used when observation_type is not set.

In this mode, the table describes activations and attributions for the submitted query rows.

Comparison mode

Comparison mode is used when observation_type is set.

The view compares the submitted query rows against a baseline observation set:

Observation typeBaseline
TRAINTraining data.
TESTTest data.
ALLTraining and test data.

In comparison mode, many activation and attribution columns are repeated with suffixes for baseline, comparison, and delta values.

Options

The Decision actions view supports options that control the table shape and optional enrichment.

OptionUse
impactful_thresholdAttribution threshold used to count impactful activations.
expandedExpand condition expressions into multiple feature, operator, and value columns.
include_estimatorsInclude estimator-level attribution statistics when the model has multiple estimators.
include_sawInclude strength and weakness columns.
include_anomaliesInclude anomaly-related columns.
winsorize_limitConfigure the winsorization limit used for error-related calculations.
scaledScale predictions and targets between 0 and 1 when supported by the view configuration.
observation_typeEnable comparison mode against TRAIN, TEST, or ALL observations.

Expanded conditions

Use expanded when you want condition expressions split into multiple columns.

This can make programmatic filtering easier, especially when you need to identify which continuous bounds or categorical conditions belong to a partition.

Estimator data

Use include_estimators when the model was trained with multiple estimators and you want to inspect partition-level attribution stability.

Estimator columns summarize the distribution of estimator attributions for each partition.

Anomaly data

Use include_anomalies to include anomaly flags for partitions.

This is useful when a partition is impactful and may also be affected by data quality, drift, or unusual distributions.

Strength and weakness data

Use include_saw to include strength and weakness columns.

These columns help identify whether the partition behaviour was weak or strong, and whether it was certain or uncertain.

Field reference

The Decision actions view returns dataframes, so individual columns are not documented as standalone API schema properties. Some columns are dynamic and depend on the model features, bins, categories, decompositions, and selected options.

Structural fields

These fields identify the partition and the rule expression.

FieldDescription
global_partition_indexIndex of the partition across the whole model.
module_indexIndex of the module that contains the partition.
module_nameName of the module that contains the partition.
condition_expr_stringCondition expression for the partition.
then_expr_stringsTHEN expressions for the partition.

Activation fields

These fields describe how often the partition activated.

FieldDescription
activationsNumber of activations for the partition.
impactful_activationsNumber of activations with attribution greater than the configured impactful_threshold.

Attribution fields

These fields describe signed attribution values for the partition.

FieldDescription
attribution_sumSum of signed attributions across rows.
attribution_meanMean signed attribution across rows.
attribution_minMinimum signed attribution.
attribution_maxMaximum signed attribution.
attribution_q1_valueFirst quartile signed attribution value.
attribution_q2_valueSecond quartile signed attribution value.
attribution_q3_valueThird quartile signed attribution value.
attribution_q4_valueFourth quartile signed attribution value.
attribution_q1_countNumber of rows in the first signed attribution quartile.
attribution_q2_countNumber of rows in the second signed attribution quartile.
attribution_q3_countNumber of rows in the third signed attribution quartile.
attribution_q4_countNumber of rows in the fourth signed attribution quartile.
attribution_q1_sumSum of signed attributions in the first quartile.
attribution_q2_sumSum of signed attributions in the second quartile.
attribution_q3_sumSum of signed attributions in the third quartile.
attribution_q4_sumSum of signed attributions in the fourth quartile.
attribution_q1_meanMean signed attribution in the first quartile.
attribution_q2_meanMean signed attribution in the second quartile.
attribution_q3_meanMean signed attribution in the third quartile.
attribution_q4_meanMean signed attribution in the fourth quartile.

Absolute attribution fields

These fields describe absolute attribution values for the partition.

FieldDescription
absolute_attribution_sumSum of absolute attributions across rows.
absolute_attribution_meanMean absolute attribution across rows.
absolute_attribution_minMinimum absolute attribution.
absolute_attribution_maxMaximum absolute attribution.
absolute_attribution_q1_valueFirst quartile absolute attribution value.
absolute_attribution_q2_valueSecond quartile absolute attribution value.
absolute_attribution_q3_valueThird quartile absolute attribution value.
absolute_attribution_q4_valueFourth quartile absolute attribution value.
absolute_attribution_q1_countNumber of rows in the first absolute attribution quartile.
absolute_attribution_q2_countNumber of rows in the second absolute attribution quartile.
absolute_attribution_q3_countNumber of rows in the third absolute attribution quartile.
absolute_attribution_q4_countNumber of rows in the fourth absolute attribution quartile.
absolute_attribution_q1_sumSum of absolute attributions in the first quartile.
absolute_attribution_q2_sumSum of absolute attributions in the second quartile.
absolute_attribution_q3_sumSum of absolute attributions in the third quartile.
absolute_attribution_q4_sumSum of absolute attributions in the fourth quartile.
absolute_attribution_q1_meanMean absolute attribution in the first quartile.
absolute_attribution_q2_meanMean absolute attribution in the second quartile.
absolute_attribution_q3_meanMean absolute attribution in the third quartile.

Dynamic condition columns

When condition expansion is enabled, the table can include dynamic columns that describe condition bounds or categorical conditions.

Column patternDescription
[<feature_name>, <high>, "", "value"]High bound value for a continuous feature condition.
[<feature_name>, <high>, "", "operator"]High bound operator for a continuous feature condition.
[<feature_name>, <low>, "", "value"]Low bound value for a continuous feature condition.
[<feature_name>, <low>, "", "operator"]Low bound operator for a continuous feature condition.
[<feature_name>, <in>, <category>, "value"]Boolean value for a categorical feature condition.
[<feature_name>, <in>, <category>, "operator"]Operator for a categorical feature condition.

Dynamic decomposition columns

The table can include dynamic columns for bin-level activations and attributions.

Column patternDescription
[<feature>, <bin_description>, "activations"]Activation count for the feature bin.
[<feature>, <bin_description>, "attribution"]Attribution value for the feature bin.
decomposition_equation(<feature>)Equation string for the decomposition associated with the feature.
decomposition_value(<feature>)Value string for the decomposition associated with the feature.

Comparison mode columns

When observation_type is set, many activation and attribution columns are returned with comparison suffixes.

SuffixDescription
_baselineValue computed from the selected baseline observation set.
_comparisonValue computed from the submitted query rows.
_deltaDifference between comparison and baseline values.

Optional fields

These fields appear when optional enrichment is requested.

Field patternAppears whenDescription
estimators_median_attribution_*include_estimatorsMedian estimator attribution statistics for the partition.
estimators_mode_attribution_*include_estimatorsMode estimator attribution statistics for the partition.
estimators_variance_attribution_*include_estimatorsVariance of estimator attribution statistics for the partition.
estimators_std_dev_attribution_*include_estimatorsStandard deviation of estimator attribution statistics for the partition.
estimators_iqr_attribution_*include_estimatorsInterquartile range of estimator attribution statistics for the partition.
imbalancedinclude_anomaliesIndicates whether the partition was flagged as imbalanced.
imbalanced_areainclude_anomaliesIndicates whether imbalance was localised to a region.
too_lowinclude_anomaliesIndicates whether the partition was flagged as too low.
too_highinclude_anomaliesIndicates whether the partition was flagged as too high.
too_wideinclude_anomaliesIndicates whether the partition spread was flagged as too wide.
weak_and_certaininclude_sawCount or flag for weak and certain behaviour.
weak_and_uncertaininclude_sawCount or flag for weak and uncertain behaviour.
strong_and_certaininclude_sawCount or flag for strong and certain behaviour.
strong_and_uncertaininclude_sawCount or flag for strong and uncertain behaviour.

justification_data

The justification_data dataframe provides supporting rows for tracing decision action table output to lower-level model structures.

The exact columns depend on the model and view configuration. Use this dataframe when you need provenance beyond the main partition-level table.