Structure
Inspect modules, partitions, rules, bins, and model metadata.
The Structure view describes a trained model.
Use it to review the model artefact after training, inspect how features were organised into modules, and understand the rules, partitions, and bins that make up the model.
When to use
| Use case | Description |
|---|---|
| Review a trained model | Check the model summary, features, targets, module count, partition count, and training parameters. |
| Inspect feature interactions | See which features appear in each module and whether a module represents a single feature or an interaction. |
| Audit rules and partitions | Inspect the conditions, expressions, and bins used by the model partitions. |
| Prepare for explanation work | Understand the model structure before using deployed model views for prediction-level explanations. |
Generate the view
Generate the Structure view from a trained model.
Response
1 { 2 "data": [ 3 { 4 "view_type": "MODEL_STRUCTURE", 5 "view_data": { 6 "summary": { 7 "columns": [ 8 "model_id", 9 "model_name", 10 "model_title", 11 "model_created", 12 "model_last_trained", 13 "model_uvc", 14 "model_intercept", 15 "has_personal_individual_data", 16 "has_reuse_restrictions", 17 "model_doi", 18 "model_copyright", 19 "n_input_features", 20 "n_transformed_features", 21 "n_output_targets", 22 "features", 23 "targets", 24 "n_modules", 25 "n_partitions", 26 "max_interaction_degree", 27 "model_interaction_count", 28 "max_width", 29 "max_depth", 30 "n_categorical_features", 31 "n_model_bins", 32 "run_params", 33 "is_sparse", 34 "module_names", 35 "n_feature_groups" 36 ], 37 "index": [ 38 0 39 ], 40 "data": [ 41 [ 42 "2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533", 43 "2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533", 44 "", 45 1769708667000, 46 1769708667000, 47 "", 48 -1.0836982274, 49 false, 50 false, 51 "", 52 "", 53 14, 54 116, 55 1, 56 [ 57 "age", 58 "workclass", 59 "fnlwgt", 60 "education", 61 "educational_45_num", 62 "marital_45_status", 63 "occupation", 64 "relationship", 65 "race", 66 "gender", 67 "capital_45_gain", 68 "capital_45_loss", 69 "hours_45_per_45_week", 70 "native_45_country" 71 ], 72 [ 73 "income" 74 ], 75 18, 76 18, 77 2, 78 23, 79 2, 80 0, 81 8, 82 203, 83 "{\"alpha\": 0.0, \"alpha_factor\": 2.0, \"bin_min_distance\": 0.0, \"binning_strategy\": \"DTREE\", \"bins\": 8, \"boosting_strategy\": \"INTERACTION_DEGREE_ASC\", \"cache_size\": 10, \"dynamic_cv\": true, \"early_stopping_patience\": 3, \"early_stopping_tolerance\": 0.0001, \"estimators\": 2, \"estimators_optimize_winsorize_level\": false, \"estimators_winsorize_level\": 0.0, \"exclude_implicit\": false, \"fine_tuning_evals\": 0, \"floatx\": \"float64\", \"init_depth\": 0, \"l1_ratio\": 0.0, \"l1_ratio_delta\": 2.0, \"learning_rate\": 1.0, \"learning_rate_decay\": 1.0, \"learning_rate_patience\": 16, \"learning_rate_tolerance\": 0.0001, \"log_interval\": 10, \"log_model\": true, \"loss_fn\": null, \"max_depth\": 3, \"max_depth_momentum\": 1, \"max_features\": 1.0, \"max_interaction_degree\": 2, \"max_interactions\": 8, \"max_iterations\": 10000, \"max_polynomial_degree\": 2, \"max_rounds\": 1, \"max_samples\": 1.0, \"model_capabilities\": {\"estimators_data\": true, \"has_causal\": false}, \"model_complexity_max_iterations\": 1, \"model_complexity_patience\": 1, \"model_complexity_tolerance\": 0.0001, \"model_constraints\": [], \"partition_min_delta\": 0.0, \"partition_min_delta_factor\": 0.5, \"partition_min_delta_fraction\": 0.4, \"partition_min_samples\": 20, \"progress_callback\": null, \"prune_patience\": 1, \"prune_threshold\": 0.0, \"regularization_patience\": 10, \"sparse_model\": true, \"subsample\": 10, \"training_size\": 0.8, \"validation_size\": 0.2}", 84 true, 85 [ 86 "age", 87 "educational_45_num", 88 "capital_45_gain", 89 "capital_45_loss", 90 "hours_45_per_45_week", 91 "education", 92 "gender", 93 "marital_45_status", 94 "native_45_country", 95 "occupation", 96 "race", 97 "relationship", 98 "workclass", 99 "occupation x relationship", 100 "education x marital_45_status", 101 "marital_45_status x occupation", 102 "capital_45_gain x relationship", 103 "educational_45_num x relationship" 104 ], 105 0 106 ] 107 ], 108 "foreign_keys": [], 109 "labels": [], 110 "types": [ 111 { 112 "data_type": "STRING" 113 }, 114 { 115 "data_type": "STRING" 116 }, 117 { 118 "data_type": "STRING" 119 }, 120 { 121 "data_type": "STRING" 122 }, 123 { 124 "data_type": "STRING" 125 }, 126 { 127 "data_type": "STRING" 128 }, 129 { 130 "data_type": "STRING" 131 }, 132 { 133 "data_type": "STRING" 134 }, 135 { 136 "data_type": "STRING" 137 }, 138 { 139 "data_type": "STRING" 140 }, 141 { 142 "data_type": "STRING" 143 }, 144 { 145 "data_type": "STRING" 146 }, 147 { 148 "data_type": "STRING" 149 }, 150 { 151 "data_type": "STRING" 152 }, 153 { 154 "data_type": "STRING" 155 }, 156 { 157 "data_type": "STRING" 158 }, 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"n_bins", 223 "bins", 224 "partition_exemplars" 225 ], 226 "index": [ 227 0, 228 1, 229 2, 230 3, 231 4, 232 5, 233 6, 234 7, 235 8, 236 9, 237 10, 238 11, 239 12, 240 13, 241 14, 242 15, 243 16, 244 17 245 ], 246 "data": [ 247 [ 248 "2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533_6ad7198fde0dfc1d7df16ae4c513db62cc38e26f82098432cbd4898f9995c8c1", 249 0, 250 "module_age", 251 "age", 252 [ 253 "age" 254 ], 255 1, 256 5, 257 6, 258 1, 259 0, 260 1, 261 1, 262 1, 263 2, 264 1, 265 1, 266 [ 267 "76770c83d8d7401d840704780725b473" 268 ], 269 "" 270 ], 271 [ 272 "2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533_a950d2fb53649d40188a531b5d352d99f5939e28b97fc02fe43a42802995dc10", 273 1, 274 "module_educational_45_num", 275 "educational_45_num", 276 [ 277 "educational_45_num" 278 ], 279 1, 280 5, 281 6, 282 1, 283 0, 284 1, 285 1, 286 1, 287 2, 288 1, 289 1, 290 [ 291 "7be739065834404cb9e5cc987d4a812e" 292 ], 293 "" 294 ], 295 [ 296 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"d1ef6a7a2c464bcd9b8b90528ace0185", 2789 "", 2790 "", 2791 "None", 2792 "None", 2793 true, 2794 "Ecuador", 2795 9, 2796 "native_45_country[Ecuador]", 2797 "{\"type_ref\": \"Symbols.CategorySymbol\", \"*args\": [\"native_45_country\", 9], \"id\": \"66a7fa9b0aff405ea9b93f5481744a66\", \"dataset_name\": \"2bcb9dc8-adult_income-root\", \"index_name\": \"Ecuador\", \"encoding\": \"ONE_HOT_ENCODING\"}", 2798 1769708, 2799 "2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533" 2800 ], 2801 [ 2802 "daf6694d509448c9bedd8b624a08999f", 2803 "occupation", 2804 "", 2805 "", 2806 "d48ef3324f004f9c840a28125ac7f704", 2807 "", 2808 "", 2809 "None", 2810 "None", 2811 true, 2812 "Tech-support", 2813 14, 2814 "occupation[Tech-support]", 2815 "{\"type_ref\": \"Symbols.CategorySymbol\", \"*args\": [\"occupation\", 14], \"id\": \"403084ad4dbe464eb3347e21649efd6d\", \"dataset_name\": \"2bcb9dc8-adult_income-root\", \"index_name\": \"Tech-support\", \"encoding\": \"ONE_HOT_ENCODING\"}", 2816 1769708, 2817 "2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533" 2818 ], 2819 [ 2820 "daf6694d509448c9bedd8b624a08999f", 2821 "capital_45_gain", 2822 "", 2823 "", 2824 "d50bcf80db5c4aabbc2e46e0d2392b12", 2825 "-INFINITY", 2826 "INFINITY", 2827 "False", 2828 "False", 2829 false, 2830 "None", 2831 "", 2832 "capital_45_gain[>-inf, <inf]", 2833 "{\"type_ref\": \"Symbols.FeatureSymbol\", \"*args\": [\"capital_45_gain\"], \"id\": \"6568e48f825b4f0380da2ab2a701e75c\", \"dataset_name\": \"2bcb9dc8-adult_income-root\"}", 2834 1769708, 2835 "2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533" 2836 ], 2837 [ 2838 "daf6694d509448c9bedd8b624a08999f", 2839 "native_45_country", 2840 "", 2841 "", 2842 "d8530c0df6be46dd978cb6b4226ecb51", 2843 "", 2844 "", 2845 "None", 2846 "None", 2847 true, 2848 "Scotland", 2849 35, 2850 "native_45_country[Scotland]", 2851 "{\"type_ref\": \"Symbols.CategorySymbol\", \"*args\": [\"native_45_country\", 35], \"id\": \"2aef23ec6b63475da875cf03d4b5f1ed\", \"dataset_name\": \"2bcb9dc8-adult_income-root\", \"index_name\": \"Scotland\", \"encoding\": \"ONE_HOT_ENCODING\"}", 2852 1769708, 2853 "2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533" 2854 ], 2855 [ 2856 "daf6694d509448c9bedd8b624a08999f", 2857 "marital_45_status", 2858 "", 2859 "", 2860 "d93d90df4fb648be9303609907304851", 2861 "", 2862 "", 2863 "None", 2864 "None", 2865 true, 2866 "Never-married", 2867 5, 2868 "marital_45_status[Never-married]", 2869 "{\"type_ref\": \"Symbols.CategorySymbol\", \"*args\": [\"marital_45_status\", 5], \"id\": \"2ecf55a9e5ec43758b9d9aba628b6098\", \"dataset_name\": \"2bcb9dc8-adult_income-root\", \"index_name\": \"Never-married\", \"encoding\": \"ONE_HOT_ENCODING\"}", 2870 1769708, 2871 "2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533" 2872 ], 2873 [ 2874 "daf6694d509448c9bedd8b624a08999f", 2875 "native_45_country", 2876 "", 2877 "", 2878 "d9b9dbf0036d4430ae95c8504782e5eb", 2879 "", 2880 "", 2881 "None", 2882 "None", 2883 true, 2884 "Canada", 2885 4, 2886 "native_45_country[Canada]", 2887 "{\"type_ref\": \"Symbols.CategorySymbol\", \"*args\": [\"native_45_country\", 4], \"id\": \"a3c6c9f70d48413bbfbef57b6093a24a\", \"dataset_name\": \"2bcb9dc8-adult_income-root\", \"index_name\": \"Canada\", \"encoding\": \"ONE_HOT_ENCODING\"}", 2888 1769708, 2889 "2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533" 2890 ], 2891 [ 2892 "daf6694d509448c9bedd8b624a08999f", 2893 "workclass", 2894 "", 2895 "", 2896 "de219c7f597e4ec38a3440592bc8441a", 2897 "", 2898 "", 2899 "None", 2900 "None", 2901 true, 2902 "?", 2903 1, 2904 "workclass[?]", 2905 "{\"type_ref\": \"Symbols.CategorySymbol\", \"*args\": [\"workclass\", 1], \"id\": \"695aa38cc58d4219aeeec33573e2fa15\", \"dataset_name\": \"2bcb9dc8-adult_income-root\", \"index_name\": \"?\", \"encoding\": \"ONE_HOT_ENCODING\"}", 2906 1769708, 2907 "2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533" 2908 ], 2909 [ 2910 "daf6694d509448c9bedd8b624a08999f", 2911 "workclass", 2912 "", 2913 "", 2914 "de6428513353417aaa86a8de6192371e", 2915 "", 2916 "", 2917 "None", 2918 "None", 2919 true, 2920 "Without-pay", 2921 9, 2922 "workclass[Without-pay]", 2923 "{\"type_ref\": \"Symbols.CategorySymbol\", \"*args\": [\"workclass\", 9], \"id\": \"7b18f33886c84400800800e47fdf4a13\", \"dataset_name\": \"2bcb9dc8-adult_income-root\", \"index_name\": \"Without-pay\", \"encoding\": \"ONE_HOT_ENCODING\"}", 2924 1769708, 2925 "2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533" 2926 ], 2927 [ 2928 "daf6694d509448c9bedd8b624a08999f", 2929 "marital_45_status", 2930 "", 2931 "", 2932 "e434c8027c9d4c35962d23a378935a3d", 2933 "", 2934 "", 2935 "None", 2936 "None", 2937 true, 2938 "Married-spouse-absent", 2939 4, 2940 "marital_45_status[Married-spouse-absent]", 2941 "{\"type_ref\": \"Symbols.CategorySymbol\", \"*args\": [\"marital_45_status\", 4], \"id\": \"c6a61f195417494c86aa8d835a977950\", \"dataset_name\": \"2bcb9dc8-adult_income-root\", \"index_name\": \"Married-spouse-absent\", \"encoding\": \"ONE_HOT_ENCODING\"}", 2942 1769708, 2943 "2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533" 2944 ], 2945 [ 2946 "daf6694d509448c9bedd8b624a08999f", 2947 "relationship", 2948 "", 2949 "", 2950 "e4ac01936b2842509129b42e3ff4b9ac", 2951 "", 2952 "", 2953 "None", 2954 "None", 2955 true, 2956 "Own-child", 2957 4, 2958 "relationship[Own-child]", 2959 "{\"type_ref\": \"Symbols.CategorySymbol\", \"*args\": [\"relationship\", 4], \"id\": \"66dc456dffd141e4a28e69ae5b0ac8a5\", \"dataset_name\": \"2bcb9dc8-adult_income-root\", \"index_name\": \"Own-child\", \"encoding\": \"ONE_HOT_ENCODING\"}", 2960 1769708, 2961 "2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533" 2962 ], 2963 [ 2964 "daf6694d509448c9bedd8b624a08999f", 2965 "native_45_country", 2966 "", 2967 "", 2968 "e90dcd67341b40f5b5b92b3a48b0b81b", 2969 "", 2970 "", 2971 "None", 2972 "None", 2973 true, 2974 "Peru", 2975 30, 2976 "native_45_country[Peru]", 2977 "{\"type_ref\": \"Symbols.CategorySymbol\", \"*args\": [\"native_45_country\", 30], \"id\": \"2f27c4d07aad4929a334e1b7889a0e94\", \"dataset_name\": \"2bcb9dc8-adult_income-root\", \"index_name\": \"Peru\", \"encoding\": \"ONE_HOT_ENCODING\"}", 2978 1769708, 2979 "2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533" 2980 ], 2981 [ 2982 "daf6694d509448c9bedd8b624a08999f", 2983 "native_45_country", 2984 "", 2985 "", 2986 "e9bdf0129f7945cb8f05f81405ac37f3", 2987 "", 2988 "", 2989 "None", 2990 "None", 2991 true, 2992 "Thailand", 2993 38, 2994 "native_45_country[Thailand]", 2995 "{\"type_ref\": \"Symbols.CategorySymbol\", \"*args\": [\"native_45_country\", 38], \"id\": \"f867577fbafe43ddb7648dd901470324\", \"dataset_name\": \"2bcb9dc8-adult_income-root\", \"index_name\": \"Thailand\", \"encoding\": \"ONE_HOT_ENCODING\"}", 2996 1769708, 2997 "2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533" 2998 ], 2999 [ 3000 "daf6694d509448c9bedd8b624a08999f", 3001 "native_45_country", 3002 "", 3003 "", 3004 "f07ecdcac25e48cb88291cc6dfd330cd", 3005 "", 3006 "", 3007 "None", 3008 "None", 3009 true, 3010 "South", 3011 36, 3012 "native_45_country[South]", 3013 "{\"type_ref\": \"Symbols.CategorySymbol\", \"*args\": [\"native_45_country\", 36], \"id\": \"3df885ffc06c45429803ddd8c22bb777\", \"dataset_name\": \"2bcb9dc8-adult_income-root\", \"index_name\": \"South\", \"encoding\": \"ONE_HOT_ENCODING\"}", 3014 1769708, 3015 "2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533" 3016 ], 3017 [ 3018 "daf6694d509448c9bedd8b624a08999f", 3019 "native_45_country", 3020 "", 3021 "", 3022 "f0d1dc1167f14914ae8b78637e4e2e5c", 3023 "", 3024 "", 3025 "None", 3026 "None", 3027 true, 3028 "Nicaragua", 3029 28, 3030 "native_45_country[Nicaragua]", 3031 "{\"type_ref\": \"Symbols.CategorySymbol\", \"*args\": [\"native_45_country\", 28], \"id\": \"ee8527244ba6458ca3078fbb217f97a4\", \"dataset_name\": \"2bcb9dc8-adult_income-root\", \"index_name\": \"Nicaragua\", \"encoding\": \"ONE_HOT_ENCODING\"}", 3032 1769708, 3033 "2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533" 3034 ], 3035 [ 3036 "daf6694d509448c9bedd8b624a08999f", 3037 "native_45_country", 3038 "", 3039 "", 3040 "f5c6aab9996e4362a42e351e0d8e2dcc", 3041 "", 3042 "", 3043 "None", 3044 "None", 3045 true, 3046 "Hong", 3047 18, 3048 "native_45_country[Hong]", 3049 "{\"type_ref\": \"Symbols.CategorySymbol\", \"*args\": [\"native_45_country\", 18], \"id\": \"771387752473460c98b92dc3934dc506\", \"dataset_name\": \"2bcb9dc8-adult_income-root\", \"index_name\": \"Hong\", \"encoding\": \"ONE_HOT_ENCODING\"}", 3050 1769708, 3051 "2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533" 3052 ], 3053 [ 3054 "daf6694d509448c9bedd8b624a08999f", 3055 "occupation", 3056 "", 3057 "", 3058 "f879827597c6480fba81a066c7932168", 3059 "", 3060 "", 3061 "None", 3062 "None", 3063 true, 3064 "Handlers-cleaners", 3065 7, 3066 "occupation[Handlers-cleaners]", 3067 "{\"type_ref\": \"Symbols.CategorySymbol\", \"*args\": [\"occupation\", 7], \"id\": \"3debf3f930784d4b91cf4cadbd4428d0\", \"dataset_name\": \"2bcb9dc8-adult_income-root\", \"index_name\": \"Handlers-cleaners\", \"encoding\": \"ONE_HOT_ENCODING\"}", 3068 1769708, 3069 "2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533" 3070 ], 3071 [ 3072 "daf6694d509448c9bedd8b624a08999f", 3073 "marital_45_status", 3074 "", 3075 "", 3076 "f98d150bd2564e44b1119cefbf239be5", 3077 "", 3078 "", 3079 "None", 3080 "None", 3081 true, 3082 "Widowed", 3083 7, 3084 "marital_45_status[Widowed]", 3085 "{\"type_ref\": \"Symbols.CategorySymbol\", \"*args\": [\"marital_45_status\", 7], \"id\": \"ae0ac88d672745979e8eaa57f00522cb\", \"dataset_name\": \"2bcb9dc8-adult_income-root\", \"index_name\": \"Widowed\", \"encoding\": \"ONE_HOT_ENCODING\"}", 3086 1769708, 3087 "2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533" 3088 ], 3089 [ 3090 "daf6694d509448c9bedd8b624a08999f", 3091 "native_45_country", 3092 "", 3093 "", 3094 "fa02faf8539042b7a923262b964dfb20", 3095 "", 3096 "", 3097 "None", 3098 "None", 3099 true, 3100 "France", 3101 12, 3102 "native_45_country[France]", 3103 "{\"type_ref\": \"Symbols.CategorySymbol\", \"*args\": [\"native_45_country\", 12], \"id\": \"35d1ff541f744f7a86d4d63b795833ff\", \"dataset_name\": \"2bcb9dc8-adult_income-root\", \"index_name\": \"France\", \"encoding\": \"ONE_HOT_ENCODING\"}", 3104 1769708, 3105 "2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533" 3106 ], 3107 [ 3108 "daf6694d509448c9bedd8b624a08999f", 3109 "relationship", 3110 "", 3111 "", 3112 "fa5946fa467c472990e5775e18d6e3fc", 3113 "", 3114 "", 3115 "None", 3116 "None", 3117 true, 3118 "Not-in-family", 3119 2, 3120 "relationship[Not-in-family]", 3121 "{\"type_ref\": \"Symbols.CategorySymbol\", \"*args\": [\"relationship\", 2], \"id\": \"08274dbe11094692ae070e3e16be435b\", \"dataset_name\": \"2bcb9dc8-adult_income-root\", \"index_name\": \"Not-in-family\", \"encoding\": \"ONE_HOT_ENCODING\"}", 3122 1769708, 3123 "2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533" 3124 ], 3125 [ 3126 "daf6694d509448c9bedd8b624a08999f", 3127 "hours_45_per_45_week", 3128 "", 3129 "", 3130 "fbb9204a1bad43e186cdbf6f978a048a", 3131 "-INFINITY", 3132 "INFINITY", 3133 "False", 3134 "False", 3135 false, 3136 "None", 3137 "", 3138 "hours_45_per_45_week[>-inf, <inf]", 3139 "{\"type_ref\": \"Symbols.FeatureSymbol\", \"*args\": [\"hours_45_per_45_week\"], \"id\": \"5a9e97880c654e40ab1d112d12bc1e7f\", \"dataset_name\": \"2bcb9dc8-adult_income-root\"}", 3140 1769708, 3141 "2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533" 3142 ], 3143 [ 3144 "daf6694d509448c9bedd8b624a08999f", 3145 "workclass", 3146 "", 3147 "", 3148 "fbf46ffeb5ed4ba0b81fc37db547e0c6", 3149 "", 3150 "", 3151 "None", 3152 "None", 3153 true, 3154 "Federal-gov", 3155 2, 3156 "workclass[Federal-gov]", 3157 "{\"type_ref\": \"Symbols.CategorySymbol\", \"*args\": [\"workclass\", 2], \"id\": \"d4edab46831449b19d11373b59ea5416\", \"dataset_name\": \"2bcb9dc8-adult_income-root\", \"index_name\": \"Federal-gov\", \"encoding\": \"ONE_HOT_ENCODING\"}", 3158 1769708, 3159 "2bcb9dc8-adult_income-root_ae51ef149e564e44aea29d4289015533" 3160 ] 3161 ], 3162 "foreign_keys": [], 3163 "labels": [], 3164 "types": [ 3165 { 3166 "data_type": "STRING" 3167 }, 3168 { 3169 "data_type": "STRING" 3170 }, 3171 { 3172 "data_type": "STRING" 3173 }, 3174 { 3175 "data_type": "STRING" 3176 }, 3177 { 3178 "data_type": "STRING" 3179 }, 3180 { 3181 "data_type": "STRING" 3182 }, 3183 { 3184 "data_type": "STRING" 3185 }, 3186 { 3187 "data_type": "STRING" 3188 }, 3189 { 3190 "data_type": "STRING" 3191 }, 3192 { 3193 "data_type": "STRING" 3194 }, 3195 { 3196 "data_type": "STRING" 3197 }, 3198 { 3199 "data_type": "STRING" 3200 }, 3201 { 3202 "data_type": "STRING" 3203 }, 3204 { 3205 "data_type": "STRING" 3206 }, 3207 { 3208 "data_type": "STRING" 3209 }, 3210 { 3211 "data_type": "STRING" 3212 } 3213 ] 3214 } 3215 }, 3216 "views_version": { 3217 "major_version": 0, 3218 "minor_version": 5, 3219 "patch_version": 0, 3220 "build_version": "dev76" 3221 }, 3222 "output_version": { 3223 "major_version": 1, 3224 "minor_version": 1, 3225 "patch_version": 1, 3226 "build_version": null 3227 } 3228 } 3229 ] 3230 }
The request only needs view_type set to MODEL_STRUCTURE.
Output
The response follows the shared Response structure format.
The Structure view returns multiple named dataframes inside view_data.
| Dataframe | Description |
|---|---|
summary | High-level model metadata, including model identifiers, features, targets, module counts, partition counts, interaction limits, bin counts, training parameters, and sparsity information. |
feature_groups | Feature group definitions, when feature groups are available for the model. |
modules | One row per module, including module identifiers, module features, interaction degree, partition counts, parameter counts, rule counts, and associated bins. |
partitions | One row per partition or rule, including the module relationship, rule identifier, condition expression, THEN expressions, partition indexes, output count, and associated bin identifiers. |
bins | Bin definitions used by the model, including the input feature, bin identifier, range bounds for continuous bins, category values for categorical bins, and bin descriptions. |
Some fields may be empty depending on the model, schema metadata, and training configuration.
Interpret the result
The Structure view is easiest to read from the top down: start with the model summary, inspect the modules, then trace modules into partitions and bins.
Start with the model summary
Use summary to understand the overall size and shape of the model.
The most useful fields are features, targets, n_modules, n_partitions, n_model_bins, max_interaction_degree, and run_params. These tell you what the model was trained to predict, which inputs it uses, and how complex the resulting structure is.
A high number of modules, partitions, or bins usually means the model has learned more structure from the data. That may be useful, but it can also make review and explanation workflows more involved.
Inspect modules
Use modules to see how the model is organised.
A module can represent a single feature or an interaction between features. Check module_features and interaction_degree to understand what each module captures. Then use n_partitions, n_parameters, n_rules, and n_bins to estimate how much structure each module contains.
This is often the best place to start when you want to understand which feature groups or interactions are most structurally important in the model.
Trace modules to partitions
Use module_id to connect rows in modules with rows in partitions.
Partitions describe the rules inside a module. Use condition_expr_string, then_expr_strings, module_partition_index, and global_partition_index to understand when a rule applies and what expression it contributes.
The condition expression describes the part of the input space where the partition applies. The THEN expression describes the contribution made when that partition is active.
Read bin definitions
Use bin_ids from partitions to find the corresponding rows in bins.
Bins describe the feature ranges or categories used by rules. Continuous bins use low, high, low_inclusive, and high_inclusive. Categorical bins use category_value and category_index.
Reading bins together with partitions helps explain how raw feature values map into the rules learned by the model.
Use the structure before explaining predictions
The Structure view describes the model artefact. It does not explain a specific prediction.
After you understand the model structure, use deployed model views to inspect how that structure behaves for inference requests, feature attribution, module attribution, diagnostics, and evaluation.
Field reference
The Structure 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 one row with high-level information about the model.
| Field | Description |
|---|---|
model_id | Unique ID of the model. |
model_name | Model name. |
model_title | Optional descriptive title for the model. |
model_created | Timestamp when the model was created. |
model_last_trained | Timestamp of the latest training run for the model. |
model_uvc | Unique Verification Code for the model, when available. |
model_intercept | Intercept value used by the model. |
has_personal_individual_data | Indicates whether the model metadata records personal or individual data. |
has_reuse_restrictions | Indicates whether the model metadata records reuse restrictions. |
model_doi | DOI or persistent identifier for the model, when available. |
model_copyright | Copyright information for the model, when available. |
n_input_features | Number of input features used by the model. |
n_transformed_features | Number of transformed features used internally by the model. |
n_output_targets | Number of output targets predicted by the model. |
features | List of input feature names. |
targets | List of output target names. |
n_modules | Number of modules in the model. |
n_partitions | Total number of partitions across the model. |
max_interaction_degree | Highest interaction degree allowed or observed in the model. |
model_interaction_count | Total number of feature interactions in the model. |
max_width | Maximum module width. |
max_depth | Maximum module depth. |
n_categorical_features | Number of categorical input features. |
n_model_bins | Total number of bins used by the model. |
run_params | Training parameters used to create the model. |
is_sparse | Whether the model is sparse. |
module_names | List of module names in the model. |
n_feature_groups | Number of feature groups associated with the model. |
feature_groups
The feature_groups dataframe lists feature groups when feature groups are available for the model.
| Field | Description |
|---|---|
feature | Name of the feature group. |
values | Features included in the feature group. |
Feature group data may be empty when the model does not include feature groups.
modules
The modules dataframe contains one row per module.
| Field | Description |
|---|---|
module_id | Unique ID of the module. |
module_index | Index of the module within the model. |
module_stable_label | Stable label for the module, when available. |
module_name | Module name. |
module_features | Input features used by the module. |
n_partitions | Number of partitions in the module. |
n_parameters | Number of parameters in the module. |
n_nonzero_parameters | Number of non-zero parameters in the module. |
n_rules | Number of rules in the module. |
module_depth | Maximum depth of the module. |
n_module_features | Number of features used by the module. |
feature_count | Count of features used by the module, when available. |
interaction_degree | Degree of feature interaction represented by the module. |
max_polynomial_degree | Highest polynomial degree used in the module expressions. |
interaction_count | Number of interactions in the module. |
n_bins | Number of bins associated with the module. |
bins | List of bin IDs associated with the module. |
partition_exemplars | Exemplar partition IDs, when available. |
partitions
The partitions dataframe contains one row per partition or rule.
| Field | Description |
|---|---|
dataset_id | ID of the dataset used to train the model. |
module_id | ID of the module that contains the partition. |
module_index | Index of the module that contains the partition. |
rule_id | Unique ID of the rule. |
group | Feature group or symbolic grouping used by the partition, when available. |
transformed_features | Features or transformed features used in the rule’s THEN expression. |
coefficients | Coefficients for the transformed features in the THEN expression. |
intercept | Intercept of the THEN expression. |
module_partition_index | Index of the partition within its module. |
global_partition_index | Index of the partition across the whole model. |
condition_expr_string | Condition expression for the rule as a string. |
then_expr_strings | THEN expressions for the rule as strings. There may be one expression per output target. |
condition_expr_object | Structured representation of the condition expression. |
then_expr_objects | Structured representation of the THEN expressions. |
bin_ids | IDs of the bins used by the rule. |
n_outputs | Number of outputs produced by the rule. |
condition_expr_friendly | Human-friendly condition expression, when available. |
condition_expr_json | JSON representation of the condition expression, when available. |
created_time | Timestamp when the partition was created. |
model_id | ID of the model that contains the partition. |
bins
The bins dataframe contains one row per bin used by the model.
| Field | Description |
|---|---|
dataset_id | ID of the dataset used to train the model. |
input_feature | Input feature associated with the bin. |
title | Human-readable bin title, when available. |
short_title | Short bin title, when available. |
bin_id | Unique ID of the bin. |
low | Lower bound for a continuous bin. |
high | Upper bound for a continuous bin. |
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 for a categorical feature. |
category_value | Category value used by the bin, for categorical bins. |
category_index | Index of the category value, for categorical bins. |
bin_description | Human-readable description of the bin. |
symbol | Symbolic representation of the bin, when available. |
created_time | Timestamp when the bin was created. |
model_id | ID of the model that contains the bin. |