Module attribution
Understand how model modules and activated rules contributed to a prediction.
The Module attribution view explains a deployed model prediction in terms of modules.
Use it to see which modules contributed to the prediction, which activated partition or rule produced each contribution, and how the module contribution breaks down across the module’s input features.
When to use
| Use case | Description |
|---|---|
| Inspect a prediction | See per-module contributions and the activated rules for a specific query row. |
| Understand feature interactions | Identify whether a contribution came from a single feature module or a module involving multiple features. |
| Debug rules and partitions | Inspect the condition and THEN expressions behind activated module partitions. |
| Estimate module importance | Run the view on a batch and aggregate absolute module attributions across rows. |
Generate the view
Generate the Module attribution 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": "MODULE_ATTRIBUTION"}],"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": "MODULE_ATTRIBUTION","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"],"index": [0,1,2,3,4,17,24,28,73,77,97,99,106,116,132,150,158,166,172,187,189,195,197],"data": [[0,"2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533_6ad7198fde0dfc1d7df16ae4c513db62cc38e26f82098432cbd4898f9995c8c1",0,"age",["age"],"daf6694d509448c9bedd8b624a08999f","e804efc3348447eba13203caa4a2f19f",0,0,"*","-0.000240974633091497*age**2 + 0.0246351995187603*age - 0.55342205229555","{\"type_ref\": \"sympy.core.add.Add\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[1, \"236b4453dfa75\", -50, 50]], \"precision\": 53}, {\"type_ref\": \"sympy.core.mul.Mul\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[0, \"c9cfc2086eadd\", -57, 52]], \"precision\": 53}, {\"type_ref\": \"Symbols.FeatureSymbol\", \"*args\": [\"age\"], \"id\": \"16472cacc1c8486e9a87cc17f5e55071\", \"dataset_name\": \"2bcb9dc8-adult_income-root\"}]}, {\"type_ref\": \"sympy.core.mul.Mul\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[1, \"1f95c4562a9365\", -65, 53]], \"precision\": 53}, {\"type_ref\": \"sympy.core.power.Pow\", \"*args\": [{\"type_ref\": \"Symbols.FeatureSymbol\", \"*args\": [\"age\"], \"id\": \"16472cacc1c8486e9a87cc17f5e55071\", \"dataset_name\": \"2bcb9dc8-adult_income-root\"}, {\"type_ref\": \"sympy.core.numbers.Integer\", \"*args\": [2]}]}]}]}","-0.000240974633091497*age**2 + 0.0246351995187603*age - 0.55342205229555","null","{\"type_ref\": \"sympy.core.add.Add\", \"*args\": [{\"type_ref\": \"sympy.core.add.Add\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[1, \"236b4453dfa75\", -50, 50]], \"precision\": 53}, {\"type_ref\": \"sympy.core.mul.Mul\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[0, \"c9cfc2086eadd\", -57, 52]], \"precision\": 53}, {\"type_ref\": \"Symbols.FeatureSymbol\", \"*args\": [\"age\"], \"id\": \"16472cacc1c8486e9a87cc17f5e55071\", \"dataset_name\": \"2bcb9dc8-adult_income-root\"}]}]}, {\"type_ref\": \"sympy.core.mul.Mul\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[1, \"1f95c4562a9365\", -65, 53]], \"precision\": 53}, {\"type_ref\": \"sympy.core.power.Pow\", \"*args\": [{\"type_ref\": \"Symbols.FeatureSymbol\", \"*args\": [\"age\"], \"id\": \"16472cacc1c8486e9a87cc17f5e55071\", \"dataset_name\": \"2bcb9dc8-adult_income-root\"}, {\"type_ref\": \"sympy.core.numbers.Integer\", \"*args\": [2]}]}]}]}","76770c83d8d7401d840704780725b473","FOR ALL",0,0,"income","age","-INFINITY","INFINITY","False","False",false,"None","age[>-inf, <inf]",1,0,"THEN_ONLY","e804efc3348447eba13203caa4a2f19f_76770c83d8d7401d840704780725b473_0",0,1769708,"2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533"],[1,"2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533_a950d2fb53649d40188a531b5d352d99f5939e28b97fc02fe43a42802995dc10",1,"educational_45_num",["educational_45_num"],"daf6694d509448c9bedd8b624a08999f","b90b1a8be22a4073aa294312092948cb",0,1,"*","0.00694509910782743*educational_45_num**2 - 0.0632974546591061*educational_45_num - 0.124711173440167","{\"type_ref\": \"sympy.core.add.Add\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[1, \"1fed124b5f0da7\", -56, 53]], \"precision\": 53}, {\"type_ref\": \"sympy.core.mul.Mul\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[0, \"71c9db62473b3\", -58, 51]], \"precision\": 53}, {\"type_ref\": \"sympy.core.power.Pow\", \"*args\": [{\"type_ref\": \"Symbols.FeatureSymbol\", \"*args\": [\"educational_45_num\"], \"id\": \"b6bcfa3897c74f2693d3b5d91e32e926\", \"dataset_name\": \"2bcb9dc8-adult_income-root\"}, {\"type_ref\": \"sympy.core.numbers.Integer\", \"*args\": [2]}]}]}, {\"type_ref\": \"sympy.core.mul.Mul\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[1, \"10344311ae4fb\", -52, 49]], \"precision\": 53}, {\"type_ref\": \"Symbols.FeatureSymbol\", \"*args\": [\"educational_45_num\"], \"id\": \"b6bcfa3897c74f2693d3b5d91e32e926\", \"dataset_name\": \"2bcb9dc8-adult_income-root\"}]}]}","0.00694509910782743*educational_45_num**2 - 0.0632974546591061*educational_45_num - 0.124711173440167","null","{\"type_ref\": \"sympy.core.add.Add\", \"*args\": [{\"type_ref\": \"sympy.core.add.Add\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[1, \"1fed124b5f0da7\", -56, 53]], \"precision\": 53}, {\"type_ref\": \"sympy.core.mul.Mul\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[1, \"10344311ae4fb\", -52, 49]], \"precision\": 53}, {\"type_ref\": \"Symbols.FeatureSymbol\", \"*args\": [\"educational_45_num\"], \"id\": \"b6bcfa3897c74f2693d3b5d91e32e926\", \"dataset_name\": \"2bcb9dc8-adult_income-root\"}]}]}, {\"type_ref\": \"sympy.core.mul.Mul\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[0, \"71c9db62473b3\", -58, 51]], \"precision\": 53}, {\"type_ref\": \"sympy.core.power.Pow\", \"*args\": [{\"type_ref\": \"Symbols.FeatureSymbol\", \"*args\": [\"educational_45_num\"], \"id\": \"b6bcfa3897c74f2693d3b5d91e32e926\", \"dataset_name\": \"2bcb9dc8-adult_income-root\"}, {\"type_ref\": \"sympy.core.numbers.Integer\", \"*args\": [2]}]}]}]}","7be739065834404cb9e5cc987d4a812e","FOR ALL",0,0,"income","educational_45_num","-INFINITY","INFINITY","False","False",false,"None","educational_45_num[>-inf, <inf]",1,0,"THEN_ONLY","b90b1a8be22a4073aa294312092948cb_7be739065834404cb9e5cc987d4a812e_0",0,1769708,"2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533"],[2,"2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533_4cffa35d90c724f76f80249ba71b7c8f0279ad9eee4b5ff151bc7e25c96e6369",2,"capital_45_gain",["capital_45_gain"],"daf6694d509448c9bedd8b624a08999f","a2d4cd5e1bc94df7922ae561c77b50ae",0,2,"*","-1.06341452398435e-9*capital_45_gain**2 + 0.000121424800667334*capital_45_gain - 0.0931422759164536","{\"type_ref\": \"sympy.core.add.Add\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[1, \"2fb05829df529\", -53, 50]], \"precision\": 53}, {\"type_ref\": \"sympy.core.mul.Mul\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[0, \"fea571877de37\", -65, 52]], \"precision\": 53}, {\"type_ref\": \"Symbols.FeatureSymbol\", \"*args\": [\"capital_45_gain\"], \"id\": \"6568e48f825b4f0380da2ab2a701e75c\", \"dataset_name\": \"2bcb9dc8-adult_income-root\"}]}, {\"type_ref\": \"sympy.core.mul.Mul\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[1, \"1244f25040d1ef\", -82, 53]], \"precision\": 53}, {\"type_ref\": \"sympy.core.power.Pow\", \"*args\": [{\"type_ref\": \"Symbols.FeatureSymbol\", \"*args\": [\"capital_45_gain\"], \"id\": \"6568e48f825b4f0380da2ab2a701e75c\", \"dataset_name\": \"2bcb9dc8-adult_income-root\"}, {\"type_ref\": \"sympy.core.numbers.Integer\", \"*args\": [2]}]}]}]}","-1.06341452398435e-9*capital_45_gain**2 + 0.000121424800667334*capital_45_gain - 0.0931422759164536","null","{\"type_ref\": \"sympy.core.add.Add\", \"*args\": [{\"type_ref\": \"sympy.core.add.Add\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[1, \"2fb05829df529\", -53, 50]], \"precision\": 53}, {\"type_ref\": \"sympy.core.mul.Mul\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[0, \"fea571877de37\", -65, 52]], \"precision\": 53}, {\"type_ref\": \"Symbols.FeatureSymbol\", \"*args\": [\"capital_45_gain\"], \"id\": \"6568e48f825b4f0380da2ab2a701e75c\", \"dataset_name\": \"2bcb9dc8-adult_income-root\"}]}]}, {\"type_ref\": \"sympy.core.mul.Mul\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[1, \"1244f25040d1ef\", -82, 53]], \"precision\": 53}, {\"type_ref\": \"sympy.core.power.Pow\", \"*args\": [{\"type_ref\": \"Symbols.FeatureSymbol\", \"*args\": [\"capital_45_gain\"], \"id\": \"6568e48f825b4f0380da2ab2a701e75c\", \"dataset_name\": \"2bcb9dc8-adult_income-root\"}, {\"type_ref\": \"sympy.core.numbers.Integer\", \"*args\": [2]}]}]}]}","d50bcf80db5c4aabbc2e46e0d2392b12","FOR ALL",0,0,"income","capital_45_gain","-INFINITY","INFINITY","False","False",false,"None","capital_45_gain[>-inf, <inf]",1,0,"THEN_ONLY","a2d4cd5e1bc94df7922ae561c77b50ae_d50bcf80db5c4aabbc2e46e0d2392b12_0",0,1769708,"2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533"],[3,"2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533_df9aa1bd9662fa4a904de1ae4fe323af3cf42907e4b35f50be40feab5966f22f",3,"capital_45_loss",["capital_45_loss"],"daf6694d509448c9bedd8b624a08999f","cd4b8aad72dc4e87b74767da8840798e",0,3,"*","1.03431672932186e-7*capital_45_loss**2 + 9.9195833362973e-5*capital_45_loss - 0.0472697231630809","{\"type_ref\": \"sympy.core.add.Add\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[1, \"1833bcb6271eb5\", -57, 53]], \"precision\": 53}, {\"type_ref\": \"sympy.core.mul.Mul\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[0, \"3401d6e1ad187\", -63, 50]], \"precision\": 53}, {\"type_ref\": \"Symbols.FeatureSymbol\", \"*args\": [\"capital_45_loss\"], \"id\": \"af43a761294a4a7780f075d31e4b896a\", \"dataset_name\": \"2bcb9dc8-adult_income-root\"}]}, {\"type_ref\": \"sympy.core.mul.Mul\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[0, \"1bc3c53bacfab3\", -76, 53]], \"precision\": 53}, {\"type_ref\": \"sympy.core.power.Pow\", \"*args\": [{\"type_ref\": \"Symbols.FeatureSymbol\", \"*args\": [\"capital_45_loss\"], \"id\": \"af43a761294a4a7780f075d31e4b896a\", \"dataset_name\": \"2bcb9dc8-adult_income-root\"}, {\"type_ref\": \"sympy.core.numbers.Integer\", \"*args\": [2]}]}]}]}","1.03431672932186e-7*capital_45_loss**2 + 9.9195833362973e-5*capital_45_loss - 0.0472697231630809","null","{\"type_ref\": \"sympy.core.add.Add\", \"*args\": [{\"type_ref\": \"sympy.core.add.Add\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[1, \"1833bcb6271eb5\", -57, 53]], \"precision\": 53}, {\"type_ref\": \"sympy.core.mul.Mul\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[0, \"3401d6e1ad187\", -63, 50]], \"precision\": 53}, {\"type_ref\": \"Symbols.FeatureSymbol\", \"*args\": [\"capital_45_loss\"], \"id\": \"af43a761294a4a7780f075d31e4b896a\", \"dataset_name\": \"2bcb9dc8-adult_income-root\"}]}]}, {\"type_ref\": \"sympy.core.mul.Mul\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[0, \"1bc3c53bacfab3\", -76, 53]], \"precision\": 53}, {\"type_ref\": \"sympy.core.power.Pow\", \"*args\": [{\"type_ref\": \"Symbols.FeatureSymbol\", \"*args\": [\"capital_45_loss\"], \"id\": \"af43a761294a4a7780f075d31e4b896a\", \"dataset_name\": \"2bcb9dc8-adult_income-root\"}, {\"type_ref\": \"sympy.core.numbers.Integer\", \"*args\": [2]}]}]}]}","8729cd66a144435fb3cc35a5a7f7ff8c","FOR ALL",0,0,"income","capital_45_loss","-INFINITY","INFINITY","False","False",false,"None","capital_45_loss[>-inf, <inf]",1,0,"THEN_ONLY","cd4b8aad72dc4e87b74767da8840798e_8729cd66a144435fb3cc35a5a7f7ff8c_0",0,1769708,"2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533"],[4,"2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533_0c44519fb8eaddc8acdc86b8c6fb95f823258dc0f34dfd010d8c7aedec64a835",4,"hours_45_per_45_week",["hours_45_per_45_week"],"daf6694d509448c9bedd8b624a08999f","118dd071191e48d6863a339ca5b62048",0,4,"*","-2.8011690775731e-5*hours_45_per_45_week**2 + 0.00773537054032227*hours_45_per_45_week - 0.268463454682358","{\"type_ref\": \"sympy.core.add.Add\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[1, \"89740abc1055f\", -53, 52]], \"precision\": 53}, {\"type_ref\": \"sympy.core.mul.Mul\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[0, \"1faf1fb7e3d815\", -60, 53]], \"precision\": 53}, {\"type_ref\": \"Symbols.FeatureSymbol\", \"*args\": [\"hours_45_per_45_week\"], \"id\": \"5a9e97880c654e40ab1d112d12bc1e7f\", \"dataset_name\": \"2bcb9dc8-adult_income-root\"}]}, {\"type_ref\": \"sympy.core.mul.Mul\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[1, \"eafaa5dc5ccbf\", -67, 52]], \"precision\": 53}, {\"type_ref\": \"sympy.core.power.Pow\", \"*args\": [{\"type_ref\": \"Symbols.FeatureSymbol\", \"*args\": [\"hours_45_per_45_week\"], \"id\": \"5a9e97880c654e40ab1d112d12bc1e7f\", \"dataset_name\": \"2bcb9dc8-adult_income-root\"}, {\"type_ref\": \"sympy.core.numbers.Integer\", \"*args\": [2]}]}]}]}","-2.8011690775731e-5*hours_45_per_45_week**2 + 0.00773537054032227*hours_45_per_45_week - 0.268463454682358","null","{\"type_ref\": \"sympy.core.add.Add\", \"*args\": [{\"type_ref\": \"sympy.core.add.Add\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[1, \"89740abc1055f\", -53, 52]], \"precision\": 53}, {\"type_ref\": \"sympy.core.mul.Mul\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[0, \"1faf1fb7e3d815\", -60, 53]], \"precision\": 53}, {\"type_ref\": \"Symbols.FeatureSymbol\", \"*args\": [\"hours_45_per_45_week\"], \"id\": \"5a9e97880c654e40ab1d112d12bc1e7f\", \"dataset_name\": \"2bcb9dc8-adult_income-root\"}]}]}, {\"type_ref\": \"sympy.core.mul.Mul\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[1, \"eafaa5dc5ccbf\", -67, 52]], \"precision\": 53}, {\"type_ref\": \"sympy.core.power.Pow\", \"*args\": [{\"type_ref\": \"Symbols.FeatureSymbol\", \"*args\": [\"hours_45_per_45_week\"], \"id\": \"5a9e97880c654e40ab1d112d12bc1e7f\", \"dataset_name\": \"2bcb9dc8-adult_income-root\"}, {\"type_ref\": \"sympy.core.numbers.Integer\", \"*args\": [2]}]}]}]}","fbb9204a1bad43e186cdbf6f978a048a","FOR ALL",0,0,"income","hours_45_per_45_week","-INFINITY","INFINITY","False","False",false,"None","hours_45_per_45_week[>-inf, <inf]",1,0,"THEN_ONLY","118dd071191e48d6863a339ca5b62048_fbb9204a1bad43e186cdbf6f978a048a_0",0,1769708,"2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533"],[17,"2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533_e0c7c5e68b525dab3fd921a8900c309f876177522b98718ad0c3f8663d4762c3",5,"education",["education"],"daf6694d509448c9bedd8b624a08999f","fd8fd44996944360a6090fcb2270db53",0,5,"*","412.379911557269*education[10th] + 412.381119924376*education[11th] + 412.386125181642*education[12th] + 412.372187592504*education[1st-4th] + 412.37935155536*education[5th-6th] + 412.378300111922*education[7th-8th] + 412.373831330137*education[9th] + 412.429154330842*education[Assoc-acdm] + 412.424291667598*education[Assoc-voc] + 412.471937492584*education[Bachelors] + 412.538012740691*education[Doctorate] + 412.4043588697*education[HS-grad] + 412.503216910576*education[Masters] + 412.359082822387*education[Preschool] + 412.553835002052*education[Prof-school] + 412.413872543326*education[Some-college] + 412.424880016362*education[__MASK__] - 412.428624571752","{\"type_ref\": \"sympy.core.add.Add\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[1, \"19c6dba5705efb\", -44, 53]], \"precision\": 53}, {\"type_ref\": \"sympy.core.mul.Mul\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[0, \"19c89bb33f619\", -40, 49]], \"precision\": 53}, {\"type_ref\": \"Symbols.CategorySymbol\", \"*args\": [\"education\", 11], \"id\": \"da61b0c799654c9b82e727f6a41fc3ec\", \"dataset_name\": \"2bcb9dc8-adult_income-root\", \"index_name\": \"Doctorate\", \"encoding\": \"ONE_HOT_ENCODING\"}]}, {\"type_ref\": \"sympy.core.mul.Mul\", \"*args\": [{\"type_ref\": \"sympy.core.numbers.Float\", \"*args\": [[0, \"33901a5a59b6f\", -41, 50]], \"precision\": 53}, {\"type_ref\": \"Symbols.CategorySymbol\", \"*args\": [\"education\", 13], \"id\": \"0dc57e21a5e94777b1b349c473e59511\", \"dataset_name\": \"2bcb9dc8-adult_income-root\", 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297.23*workclass[?]"],["2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533_6ad7198fde0dfc1d7df16ae4c513db62cc38e26f82098432cbd4898f9995c8c1",0,"age",0,0,"e804efc3348447eba13203caa4a2f19f",0,0.0647808541,0.0551064515,"age",0,"None",0,0.0647808541,0,"58","None","FOR ALL","-0.55342 + 0.024635*age - 0.00024097*age**2.0000"],["2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533_df9aa1bd9662fa4a904de1ae4fe323af3cf42907e4b35f50be40feab5966f22f",3,"capital_45_loss",0,3,"cd4b8aad72dc4e87b74767da8840798e",0,-0.0472697232,0.0402104409,"capital_45_loss",0,"None",0,-0.0472697232,0,"0","None","FOR ALL","-0.047270"],["2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533_0c44519fb8eaddc8acdc86b8c6fb95f823258dc0f34dfd010d8c7aedec64a835",4,"hours_45_per_45_week",0,4,"118dd071191e48d6863a339ca5b62048",0,-0.032039807,0.0272549674,"hours_45_per_45_week",0,"None",0,-0.032039807,0,"35","None","FOR ALL","-0.26846 + 0.0077354*hours_45_per_45_week - 2.8012e-5*hours_45_per_45_week**2.0000"],["2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533_ce211f54887c1b0b06416d57190c3c71a958e15d21c5b4394f21c965176aaa06",17,"educational_45_num x relationship",0,17,"6186aaa286694637b88b5caabf982a5a",0,0.0300657735,0.025575737,"educational_45_num",0,"relationship",0,0.0201737068,0.0098920667,"9","Husband","FOR ALL","-0.21405 + 0.080317*relationship[Husband] + 0.018200*educational_45_num"],["2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533_de38f0041deaea0e906dc02f9bbdacaf31c4426ba3dbe1d5b1474c719ce24204",16,"capital_45_gain x relationship",0,16,"0331df6d07e74760a36abeb0de72397d",0,0.0253621205,0.0215745297,"relationship",0,"capital_45_gain",0,0.0253621205,0,"Husband","0","FOR ALL","-0.027105 + 0.052467*relationship[Husband]"],["2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533_e0c7c5e68b525dab3fd921a8900c309f876177522b98718ad0c3f8663d4762c3",5,"education",0,5,"fd8fd44996944360a6090fcb2270db53",0,-0.0242657021,0.0206418509,"education",0,"None",0,-0.0242657021,0,"HS-grad","None","FOR ALL","-412.43 + 412.40*education[HS-grad]"],["2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533_3b4ae933141b8f2f55eb696a195d529e0b1344839df143ed7715e540e07fafd8",14,"education x marital_45_status",0,14,"96f95af0d80f4a719d86505db18b33e7",0,0.0240163011,0.0204296957,"marital_45_status",0,"education",0,0.0311475476,-0.0071312464,"Married-civ-spouse","HS-grad","FOR ALL","1085.4 + 322.27*education[HS-grad] - 1407.6*marital_45_status[Married-civ-spouse]"],["2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533_b6f3351fee9d7682c2b76a2b7f37de91f6cd06bf32285cffb220d58dc5399668",15,"marital_45_status x 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null},{"data_type": "NUMBER","format": "INT64"},{"data_type": "NUMBER","format": "INT64"},{"data_type": "STRING","format": null},{"data_type": "NUMBER","format": "INT64"},{"data_type": "NUMBER","format": "FLOAT64"},{"data_type": "NUMBER","format": "FLOAT64"},{"data_type": "STRING","format": null},{"data_type": "ANY"},{"data_type": "STRING","format": null},{"data_type": "ANY"},{"data_type": "NUMBER","format": "FLOAT64"},{"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}]},"views_version": {"major_version": 0,"minor_version": 5,"patch_version": 0,"build_version": "dev76"}}]}
The request needs view_type set to MODULE_ATTRIBUTION and query data aligned with the deployed model input schema.
Output
The response follows the shared Response structure format.
The Module attribution view returns two dataframes.
| Dataframe | Description |
|---|---|
view_data | The module attribution table. Each row represents an attributed module partition for a target output. |
justification_data | Supporting provenance data that links module attributions back to decompositions, bins, rules, and model structure. |
Interpret the result
The Module attribution view is easiest to read by ranking modules first, then opening the rule and feature-level columns for the rows that matter most.
Rank module impact
Use total_attribution_normalized to find the most influential rows.
This field is based on the absolute contribution, so it is useful for ranking module impact regardless of whether the module increased or decreased the prediction.
Use total_attribution when you need the signed contribution. Positive values push the prediction upward. Negative values push it downward.
Identify the module and partition
Use module_id, module_index, module_name, module_partition_index, and global_partition_index to identify where the contribution came from.
The module fields identify the part of the model that contributed. The partition fields identify the activated partition or rule inside that module.
Inspect the rule logic
Use condition_expr_friendly to understand when the partition applies.
Use summarized_then_expr to understand the expression that contributed when the partition was active.
For a single-row query, this is often the fastest way to explain why a module contributed to a prediction.
Understand feature contributions inside the module
Use the indexed feature columns to see which features participated in the module contribution.
Columns such as input_feature_0, input_feature_1, feature_attribution_0, feature_attribution_1, feature_input_0, and feature_input_1 describe the features involved in the module and their individual contributions.
The number of indexed feature columns depends on the module. Single-feature modules usually use the _0 columns. Interaction modules can use additional indexes.
Trace the attribution source
Use justification_data when you need to trace the module attribution to the lower-level structures that produced it.
The justification table connects module attribution rows to decompositions, bins, modules, partitions, and rule expressions. Use it when you need provenance beyond the user-facing module attribution table.
Move from local to global
The Module attribution view explains one query row or batch of query rows.
To estimate module importance, run the view over a representative batch and aggregate the absolute value of total_attribution by module. This produces a model-level summary of which modules and interactions matter most across the batch.
Example visualisations
Options
The Module attribution view supports optional fields that enrich or reshape the output.
| Option | Use |
|---|---|
include_model_intercept | Include the model intercept as an attribution row. |
encode_expressions | Include JSON-encoded expression fields. |
rollup | Roll up categorical sub-components. |
include_estimators | Include estimator-level attribution statistics when the model has multiple estimators. |
include_saw | Include strength and weakness columns. |
include_anomalies | Include anomaly-related columns. |
include_titles | Include title-based columns when feature descriptions are available. |
feature_descriptions | Provide descriptions that add context to features. |
winsorize_limit | Configure the winsorization limit used for error-related calculations. |
scaled | Scale predictions and targets between 0 and 1 when supported by the view configuration. |
Model intercept
Use include_model_intercept when you want the model intercept to appear alongside module attributions.
The intercept row can help reconcile the sum of module attributions with the prediction output.
Encoded expressions
Use encode_expressions when you need expression data in a machine-readable JSON form.
This is useful when building custom tooling that parses or renders rule expressions instead of displaying them as plain strings.
Rollup
Use rollup when you want categorical sub-components combined into a more compact module-level output.
This can make the table easier to read when categorical modules produce many lower-level components.
Estimator data
Use include_estimators when the model was trained with multiple estimators and you want to understand attribution stability across them.
Estimator statistics help distinguish modules with consistently strong effects from modules whose effects vary across estimators.
Anomaly data
Use include_anomalies to include anomaly flags for attributed module partitions.
This is useful when a module has a large contribution and may also be affected by data quality, drift, or unusual feature distribution patterns.
Strength and weakness data
Use include_saw to include strength and weakness columns.
These columns help identify whether the contributing module behaviour was weak or strong, and whether it was certain or uncertain.
Titles and feature descriptions
Use include_titles together with feature_descriptions when you want the response to include user-facing titles, short titles, notes, or descriptions.
This is useful for charts, tables, and explanations where raw feature names or module names are too technical.
Field reference
The Module attribution view returns dataframes, so individual columns are not documented as standalone API schema properties. Some columns are optional and only appear when the corresponding option is enabled.
view_data
The view_data dataframe contains the module attribution output.
| Field | Description |
|---|---|
module_id | Unique ID of the module. |
module_index | Index of the module within the model. |
module_name | Name of the module. |
module_partition_index | Index of the partition within the module. |
global_partition_index | Index of the partition across the whole model. |
rule_id | ID of the activated rule. |
output_target_index | Index of the output target for the attribution. |
total_attribution | Signed attribution value for the module row. |
total_attribution_normalized | Absolute attribution normalized for comparison across rows. |
condition_expr_friendly | Human-friendly condition expression for the activated partition. |
summarized_then_expr | Summarized THEN expression for the activated partition. |
Indexed feature fields
Module attribution rows can include indexed feature fields. The number of indexes depends on the module’s interaction degree.
| Field pattern | Description |
|---|---|
input_feature_<index> | Feature name at the given position in the module. |
grouped_features_<index> | Feature group associated with the indexed feature, when available. |
feature_attribution_<index> | Attribution assigned to the indexed feature within the module row. |
feature_input_<index> | Query input value for the indexed feature. |
Optional view_data fields
These fields appear when optional enrichment is requested.
| Field | Appears when | Description |
|---|---|---|
summarized_then_expr_json | encode_expressions | JSON-encoded summarized THEN expression. |
total_median_estimators_attribution | include_estimators | Median total attribution across estimators. |
total_mode_estimators_attribution | include_estimators | Mode total attribution across estimators. |
total_variance_estimators_attribution | include_estimators | Variance of total attribution across estimators. |
total_std_dev_estimators_attribution | include_estimators | Standard deviation of total attribution across estimators. |
total_iqr_estimators_attribution | include_estimators | Interquartile range of total attribution across estimators. |
imbalanced | include_anomalies | Indicates whether the module partition was flagged as imbalanced. |
imbalanced_area | include_anomalies | Indicates whether imbalance was localised to a region. |
too_low | include_anomalies | Indicates whether the module partition was flagged as too low. |
too_high | include_anomalies | Indicates whether the module partition was flagged as too high. |
too_wide | include_anomalies | Indicates whether the module partition spread was flagged as too wide. |
weak_and_certain | include_saw | Count of weak and certain activated components associated with the module row. |
weak_and_uncertain | include_saw | Count of weak and uncertain activated components associated with the module row. |
strong_and_certain | include_saw | Count of strong and certain activated components associated with the module row. |
strong_and_uncertain | include_saw | Count of strong and uncertain activated components associated with the module row. |
feature_title | include_titles | Human-readable feature title, when title data is available. |
feature_short_title | include_titles | Short feature label suitable for charts or compact tables. |
feature_notes | include_titles | Notes associated with the feature. |
feature_description | include_titles | Description associated with the feature. |
justification_data
The justification_data dataframe links module attribution rows to the model structures that produced them.
| Field | Description |
|---|---|
decomposition_index | Index of the decomposition component associated with the module attribution. |
module_id | ID of the module associated with the attribution. |
module_index | Index of the module associated with the attribution. |
module_name | Name of the module associated with the attribution. |
module_features | Features used by the module. |
dataset_id | ID of the dataset associated with the supporting rule or bin, when available. |
rule_id | ID of the rule associated with the attribution. |
module_partition_index | Index of the partition within its module. |
global_partition_index | Index of the partition across the model. |
condition_expr_string | Condition expression for the supporting rule. |
decomposed_then_expr_string | Decomposed THEN expression associated with the attribution, when available. |
then_expr_string | THEN expression associated with the supporting rule. |
condition_expr_object | Structured representation of the condition expression, when available. |
then_expr_object | Structured representation of the THEN expression, when available. |
bin_id | ID of the bin associated with the attribution. |
condition_expr_friendly | Human-friendly condition expression, when available. |
condition_expr_json | JSON representation of the condition expression, when available. |
output_target_index | Index of the output target associated with the attribution. |
output_target_name | Name of the output target associated with the attribution. |
input_feature | Input feature associated with the attribution. |
low | Lower bound of the bin for continuous features, when available. |
high | Upper bound of the bin for continuous features, when available. |
low_inclusive | Whether the lower bound is included in the bin interval. |
high_inclusive | Whether the upper bound is included in the bin interval. |
is_categorical | Whether the bin is categorical. |
category_value | Category value for categorical bins. |
bin_description | Human-readable bin description. |
n_module_features | Number of features in the module. |
module_feature_index | Index of the feature within the module. |
feature_participation | How the feature participates in the decomposition. |
decomposition_id | ID of the decomposition associated with the attribution. |
feature_bin_index | Index of the feature bin associated with the attribution. |
created_time | Timestamp or creation marker for the supporting structure, when available. |
model_id | ID of the model associated with the attribution. |