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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    TypeError
Message:      Couldn't cast array of type list<item: int64> to int64
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2303, in cast_table_to_schema
                  cast_array_to_feature(
                  ~~~~~~~~~~~~~~~~~~~~~^
                      table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                      feature,
                      ^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1852, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ~~~~^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2143, in cast_array_to_feature
                  return array_cast(
                      array,
                  ...<2 lines>...
                      allow_decimal_to_str=allow_decimal_to_str,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1854, in wrapper
                  return func(array, *args, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2007, in array_cast
                  raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
              TypeError: Couldn't cast array of type list<item: int64> to int64
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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model
list
dataset
string
modality
string
row_no
int64
features
dict
target
dict
predicted
dict
example
string
q_type
int64
q
string
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
21,414
{ "age": 57, "workclass": "Local-gov", "fnlwgt": 44273, "education": "HS-grad", "education-num": 9, "marital-status": "Widowed", "occupation": "Transport-moving", "relationship": "Not-in-family", "race": "White", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "n...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
18,785
{ "age": 61, "workclass": "Private", "fnlwgt": 146788, "education": "7th-8th", "education-num": 4, "marital-status": "Married-civ-spouse", "occupation": "Transport-moving", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, ...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
29,811
{ "age": 39, "workclass": "Private", "fnlwgt": 174330, "education": "HS-grad", "education-num": 9, "marital-status": "Separated", "occupation": "Craft-repair", "relationship": "Unmarried", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "native-cou...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
34,486
{ "age": 35, "workclass": "Private", "fnlwgt": 27408, "education": "Some-college", "education-num": 10, "marital-status": "Never-married", "occupation": "Sales", "relationship": "Not-in-family", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "nati...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
32,162
{ "age": 30, "workclass": "Private", "fnlwgt": 302473, "education": "HS-grad", "education-num": 9, "marital-status": "Never-married", "occupation": "Adm-clerical", "relationship": "Own-child", "race": "White", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "nati...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
15,313
{ "age": 27, "workclass": "Private", "fnlwgt": 160291, "education": "Some-college", "education-num": 10, "marital-status": "Never-married", "occupation": "Adm-clerical", "relationship": "Unmarried", "race": "Black", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, ...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
15,176
{ "age": 36, "workclass": "Private", "fnlwgt": 150057, "education": "Bachelors", "education-num": 13, "marital-status": "Married-civ-spouse", "occupation": "Prof-specialty", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 50, ...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
45,653
{ "age": 42, "workclass": "Private", "fnlwgt": 22831, "education": "HS-grad", "education-num": 9, "marital-status": "Never-married", "occupation": "Other-service", "relationship": "Not-in-family", "race": "White", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
9,800
{ "age": 17, "workclass": "Private", "fnlwgt": 147069, "education": "11th", "education-num": 7, "marital-status": "Never-married", "occupation": "Other-service", "relationship": "Own-child", "race": "White", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 16, "native...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
28,387
{ "age": 33, "workclass": "State-gov", "fnlwgt": 174171, "education": "Some-college", "education-num": 10, "marital-status": "Separated", "occupation": "Tech-support", "relationship": "Not-in-family", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 12, ...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
37,795
{ "age": 40, "workclass": "Private", "fnlwgt": 124692, "education": "Bachelors", "education-num": 13, "marital-status": "Married-civ-spouse", "occupation": "Exec-managerial", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, ...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
38,572
{ "age": 35, "workclass": "Private", "fnlwgt": 188972, "education": "HS-grad", "education-num": 9, "marital-status": "Widowed", "occupation": "Exec-managerial", "relationship": "Unmarried", "race": "White", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 30, "native-...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
38,457
{ "age": 22, "workclass": "Private", "fnlwgt": 137591, "education": "Some-college", "education-num": 10, "marital-status": "Never-married", "occupation": "Sales", "relationship": "Own-child", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 35, "native-...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
24,057
{ "age": 27, "workclass": "Private", "fnlwgt": 109997, "education": "HS-grad", "education-num": 9, "marital-status": "Divorced", "occupation": "Other-service", "relationship": "Not-in-family", "race": "White", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "nati...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
8,688
{ "age": 68, "workclass": "Self-emp-not-inc", "fnlwgt": 150904, "education": "HS-grad", "education-num": 9, "marital-status": "Widowed", "occupation": "Craft-repair", "relationship": "Not-in-family", "race": "White", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 35, ...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
33,515
{ "age": 57, "workclass": "Private", "fnlwgt": 266189, "education": "HS-grad", "education-num": 9, "marital-status": "Divorced", "occupation": "Adm-clerical", "relationship": "Unmarried", "race": "White", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 42, "native-co...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
47,243
{ "age": 35, "workclass": "Private", "fnlwgt": 301862, "education": "HS-grad", "education-num": 9, "marital-status": "Never-married", "occupation": "Craft-repair", "relationship": "Unmarried", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 50, "native...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
45,037
{ "age": 25, "workclass": "Private", "fnlwgt": 167031, "education": "Some-college", "education-num": 10, "marital-status": "Never-married", "occupation": "Other-service", "relationship": "Other-relative", "race": "Other", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week":...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
29,415
{ "age": 41, "workclass": "State-gov", "fnlwgt": 180272, "education": "Masters", "education-num": 14, "marital-status": "Never-married", "occupation": "Prof-specialty", "relationship": "Own-child", "race": "White", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 35, ...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
32,048
{ "age": 35, "workclass": "Private", "fnlwgt": 81232, "education": "Bachelors", "education-num": 13, "marital-status": "Married-civ-spouse", "occupation": "Sales", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 15024, "capital-loss": 0, "hours-per-week": 50, "nati...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
30,669
{ "age": 21, "workclass": "Private", "fnlwgt": 179720, "education": "HS-grad", "education-num": 9, "marital-status": "Never-married", "occupation": "Other-service", "relationship": "Other-relative", "race": "White", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 30, ...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
26,148
{ "age": 37, "workclass": "Private", "fnlwgt": 227545, "education": "Some-college", "education-num": 10, "marital-status": "Married-civ-spouse", "occupation": "Sales", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 44, "nati...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
47,537
{ "age": 52, "workclass": "Self-emp-not-inc", "fnlwgt": 34973, "education": "HS-grad", "education-num": 9, "marital-status": "Married-civ-spouse", "occupation": "Farming-fishing", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 1887, "hours-per-wee...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
40,160
{ "age": 38, "workclass": "State-gov", "fnlwgt": 188303, "education": "Some-college", "education-num": 10, "marital-status": "Married-civ-spouse", "occupation": "Protective-serv", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 7688, "capital-loss": 0, "hours-per-wee...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
6,403
{ "age": 46, "workclass": "Private", "fnlwgt": 411595, "education": "5th-6th", "education-num": 3, "marital-status": "Widowed", "occupation": "Machine-op-inspct", "relationship": "Unmarried", "race": "White", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "nativ...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
27,209
{ "age": 23, "workclass": "Private", "fnlwgt": 113466, "education": "HS-grad", "education-num": 9, "marital-status": "Never-married", "occupation": "Craft-repair", "relationship": "Not-in-family", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "na...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
10,348
{ "age": 58, "workclass": "Private", "fnlwgt": 141379, "education": "HS-grad", "education-num": 9, "marital-status": "Divorced", "occupation": "Adm-clerical", "relationship": "Not-in-family", "race": "White", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 42, "nativ...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
3,280
{ "age": 18, "workclass": "Private", "fnlwgt": 122988, "education": "HS-grad", "education-num": 9, "marital-status": "Never-married", "occupation": "Handlers-cleaners", "relationship": "Own-child", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 20, "n...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
39,579
{ "age": 21, "workclass": "Private", "fnlwgt": 83704, "education": "12th", "education-num": 8, "marital-status": "Married-civ-spouse", "occupation": "Craft-repair", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "native-...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
23,484
{ "age": 40, "workclass": "Self-emp-not-inc", "fnlwgt": 238574, "education": "Prof-school", "education-num": 15, "marital-status": "Married-civ-spouse", "occupation": "Prof-specialty", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-w...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
25,727
{ "age": 28, "workclass": "Private", "fnlwgt": 398220, "education": "5th-6th", "education-num": 3, "marital-status": "Never-married", "occupation": "Craft-repair", "relationship": "Other-relative", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "n...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
39,598
{ "age": 33, "workclass": "Private", "fnlwgt": 246038, "education": "Bachelors", "education-num": 13, "marital-status": "Married-civ-spouse", "occupation": "Prof-specialty", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, ...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
6,742
{ "age": 41, "workclass": "Private", "fnlwgt": 160893, "education": "Assoc-acdm", "education-num": 12, "marital-status": "Never-married", "occupation": "Adm-clerical", "relationship": "Not-in-family", "race": "White", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 45,...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
32,508
{ "age": 54, "workclass": "Private", "fnlwgt": 210736, "education": "HS-grad", "education-num": 9, "marital-status": "Married-civ-spouse", "occupation": "Craft-repair", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "nat...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
6,255
{ "age": 40, "workclass": "Self-emp-not-inc", "fnlwgt": 145441, "education": "Some-college", "education-num": 10, "marital-status": "Divorced", "occupation": "Exec-managerial", "relationship": "Unmarried", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": ...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
47,661
{ "age": 47, "workclass": "Private", "fnlwgt": 252079, "education": "Bachelors", "education-num": 13, "marital-status": "Married-civ-spouse", "occupation": "Machine-op-inspct", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 7688, "capital-loss": 0, "hours-per-week":...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
13,808
{ "age": 49, "workclass": "Private", "fnlwgt": 118520, "education": "HS-grad", "education-num": 9, "marital-status": "Divorced", "occupation": "Adm-clerical", "relationship": "Not-in-family", "race": "White", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 45, "nativ...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
22,639
{ "age": 45, "workclass": "Local-gov", "fnlwgt": 148222, "education": "HS-grad", "education-num": 9, "marital-status": "Never-married", "occupation": "Adm-clerical", "relationship": "Not-in-family", "race": "Black", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, ...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
42,663
{ "age": 47, "workclass": "Private", "fnlwgt": 431515, "education": "Assoc-voc", "education-num": 11, "marital-status": "Married-civ-spouse", "occupation": "Craft-repair", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
18,148
{ "age": 36, "workclass": "Federal-gov", "fnlwgt": 128884, "education": "HS-grad", "education-num": 9, "marital-status": "Divorced", "occupation": "Adm-clerical", "relationship": "Not-in-family", "race": "White", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 48, "n...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
21,762
{ "age": 19, "workclass": "Self-emp-not-inc", "fnlwgt": 30800, "education": "10th", "education-num": 6, "marital-status": "Married-spouse-absent", "occupation": "Adm-clerical", "relationship": "Unmarried", "race": "Amer-Indian-Eskimo", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "ho...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
20,071
{ "age": 47, "workclass": "Private", "fnlwgt": 140664, "education": "Bachelors", "education-num": 13, "marital-status": "Divorced", "occupation": "Sales", "relationship": "Not-in-family", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 45, "native-coun...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
39,213
{ "age": 23, "workclass": "Private", "fnlwgt": 520759, "education": "HS-grad", "education-num": 9, "marital-status": "Never-married", "occupation": "Other-service", "relationship": "Not-in-family", "race": "Black", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 30, "n...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
28,662
{ "age": 44, "workclass": "State-gov", "fnlwgt": 691903, "education": "Masters", "education-num": 14, "marital-status": "Married-civ-spouse", "occupation": "Prof-specialty", "relationship": "Husband", "race": "Black", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 60, ...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
7,234
{ "age": 40, "workclass": "Self-emp-inc", "fnlwgt": 115411, "education": "Assoc-acdm", "education-num": 12, "marital-status": "Married-civ-spouse", "occupation": "Exec-managerial", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week"...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
42,284
{ "age": 33, "workclass": "Private", "fnlwgt": 341187, "education": "Bachelors", "education-num": 13, "marital-status": "Married-civ-spouse", "occupation": "Exec-managerial", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 50, ...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
46,692
{ "age": 29, "workclass": "Private", "fnlwgt": 327779, "education": "Some-college", "education-num": 10, "marital-status": "Married-civ-spouse", "occupation": "Handlers-cleaners", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week":...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
48,236
{ "age": 46, "workclass": "Local-gov", "fnlwgt": 267952, "education": "Assoc-voc", "education-num": 11, "marital-status": "Divorced", "occupation": "Exec-managerial", "relationship": "Not-in-family", "race": "White", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 36, ...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
42,099
{ "age": 45, "workclass": "Local-gov", "fnlwgt": 235431, "education": "HS-grad", "education-num": 9, "marital-status": "Separated", "occupation": "Other-service", "relationship": "Unmarried", "race": "Black", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "nativ...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
20,145
{ "age": 50, "workclass": "State-gov", "fnlwgt": 229272, "education": "HS-grad", "education-num": 9, "marital-status": "Married-civ-spouse", "occupation": "Craft-repair", "relationship": "Husband", "race": "Black", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "n...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
23,205
{ "age": 38, "workclass": "Private", "fnlwgt": 119177, "education": "Bachelors", "education-num": 13, "marital-status": "Married-civ-spouse", "occupation": "Sales", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "native-...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
45,406
{ "age": 24, "workclass": "Private", "fnlwgt": 143766, "education": "Some-college", "education-num": 10, "marital-status": "Never-married", "occupation": "Machine-op-inspct", "relationship": "Own-child", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 55...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
38,810
{ "age": 22, "workclass": "Private", "fnlwgt": 185452, "education": "Bachelors", "education-num": 13, "marital-status": "Never-married", "occupation": "Exec-managerial", "relationship": "Own-child", "race": "White", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, ...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
34,749
{ "age": 34, "workclass": "Private", "fnlwgt": 209691, "education": "Assoc-voc", "education-num": 11, "marital-status": "Married-civ-spouse", "occupation": "Prof-specialty", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 4386, "capital-loss": 0, "hours-per-week": 50...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
24,519
{ "age": 62, "workclass": "Private", "fnlwgt": 113080, "education": "7th-8th", "education-num": 4, "marital-status": "Divorced", "occupation": "Craft-repair", "relationship": "Own-child", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "native-coun...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
17,704
{ "age": 41, "workclass": "Private", "fnlwgt": 184102, "education": "11th", "education-num": 7, "marital-status": "Divorced", "occupation": "Other-service", "relationship": "Not-in-family", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "native-co...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
11,716
{ "age": 25, "workclass": "Private", "fnlwgt": 161631, "education": "Some-college", "education-num": 10, "marital-status": "Married-civ-spouse", "occupation": "Craft-repair", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, ...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
42,725
{ "age": 43, "workclass": "Private", "fnlwgt": 76460, "education": "HS-grad", "education-num": 9, "marital-status": "Never-married", "occupation": "Adm-clerical", "relationship": "Not-in-family", "race": "White", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "n...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
609
{ "age": 33, "workclass": "Local-gov", "fnlwgt": 217304, "education": "Bachelors", "education-num": 13, "marital-status": "Never-married", "occupation": "Adm-clerical", "relationship": "Not-in-family", "race": "Black", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, ...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
11,823
{ "age": 35, "workclass": "Private", "fnlwgt": 282753, "education": "Assoc-voc", "education-num": 11, "marital-status": "Married-civ-spouse", "occupation": "Craft-repair", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
44,599
{ "age": 38, "workclass": "Self-emp-not-inc", "fnlwgt": 194534, "education": "Masters", "education-num": 14, "marital-status": "Married-civ-spouse", "occupation": "Prof-specialty", "relationship": "Husband", "race": "Black", "sex": "Male", "capital-gain": 99999, "capital-loss": 0, "hours-per-w...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
39,633
{ "age": 28, "workclass": "Private", "fnlwgt": 207513, "education": "HS-grad", "education-num": 9, "marital-status": "Never-married", "occupation": "Sales", "relationship": "Own-child", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 48, "native-countr...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
17,088
{ "age": 33, "workclass": "Private", "fnlwgt": 169879, "education": "Bachelors", "education-num": 13, "marital-status": "Married-civ-spouse", "occupation": "Prof-specialty", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 3103, "capital-loss": 0, "hours-per-week": 47...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
20,270
{ "age": 22, "workclass": "Private", "fnlwgt": 191324, "education": "Some-college", "education-num": 10, "marital-status": "Never-married", "occupation": "Protective-serv", "relationship": "Own-child", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 25, ...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
42,112
{ "age": 31, "workclass": "Private", "fnlwgt": 147284, "education": "Doctorate", "education-num": 16, "marital-status": "Married-civ-spouse", "occupation": "Prof-specialty", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 1977, "hours-per-week": 99...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
4,484
{ "age": 18, "workclass": "Private", "fnlwgt": 217942, "education": "11th", "education-num": 7, "marital-status": "Never-married", "occupation": "Other-service", "relationship": "Own-child", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 24, "native-c...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
34,474
{ "age": 52, "workclass": "Private", "fnlwgt": 110748, "education": "Masters", "education-num": 14, "marital-status": "Married-civ-spouse", "occupation": "Prof-specialty", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 50, "...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
20,488
{ "age": 64, "workclass": "Private", "fnlwgt": 321166, "education": "Bachelors", "education-num": 13, "marital-status": "Divorced", "occupation": "Sales", "relationship": "Not-in-family", "race": "White", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 5, "native-cou...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
40,272
{ "age": 62, "workclass": "Private", "fnlwgt": 345780, "education": "Assoc-voc", "education-num": 11, "marital-status": "Divorced", "occupation": "Other-service", "relationship": "Not-in-family", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "nat...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
37,771
{ "age": 19, "workclass": "Private", "fnlwgt": 146679, "education": "Some-college", "education-num": 10, "marital-status": "Never-married", "occupation": "Exec-managerial", "relationship": "Own-child", "race": "Black", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 30, ...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
44,934
{ "age": 22, "workclass": "Private", "fnlwgt": 315974, "education": "Some-college", "education-num": 10, "marital-status": "Never-married", "occupation": "Sales", "relationship": "Not-in-family", "race": "White", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "n...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
15,970
{ "age": 38, "workclass": "Federal-gov", "fnlwgt": 455379, "education": "12th", "education-num": 8, "marital-status": "Married-civ-spouse", "occupation": "Protective-serv", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 56, ...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
28,750
{ "age": 27, "workclass": "Private", "fnlwgt": 256764, "education": "Assoc-acdm", "education-num": 12, "marital-status": "Never-married", "occupation": "Sales", "relationship": "Not-in-family", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "nativ...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
33,876
{ "age": 33, "workclass": "Private", "fnlwgt": 301867, "education": "Some-college", "education-num": 10, "marital-status": "Never-married", "occupation": "Adm-clerical", "relationship": "Unmarried", "race": "Asian-Pac-Islander", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
12,740
{ "age": 22, "workclass": "Private", "fnlwgt": 193190, "education": "HS-grad", "education-num": 9, "marital-status": "Never-married", "occupation": "Other-service", "relationship": "Own-child", "race": "Black", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "nat...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
3,797
{ "age": 39, "workclass": "Private", "fnlwgt": 346478, "education": "HS-grad", "education-num": 9, "marital-status": "Married-civ-spouse", "occupation": "Sales", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "native-cou...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
7,814
{ "age": 36, "workclass": "Private", "fnlwgt": 120204, "education": "HS-grad", "education-num": 9, "marital-status": "Divorced", "occupation": "Tech-support", "relationship": "Not-in-family", "race": "White", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "nativ...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
12,311
{ "age": 24, "workclass": "Private", "fnlwgt": 88824, "education": "Bachelors", "education-num": 13, "marital-status": "Never-married", "occupation": "Tech-support", "relationship": "Not-in-family", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
12,899
{ "age": 44, "workclass": "Local-gov", "fnlwgt": 185267, "education": "Bachelors", "education-num": 13, "marital-status": "Married-civ-spouse", "occupation": "Prof-specialty", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 1902, "hours-per-week": ...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
22,634
{ "age": 29, "workclass": "Private", "fnlwgt": 301031, "education": "HS-grad", "education-num": 9, "marital-status": "Married-civ-spouse", "occupation": "Transport-moving", "relationship": "Husband", "race": "Black", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, ...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
14,472
{ "age": 23, "workclass": "Private", "fnlwgt": 114939, "education": "Some-college", "education-num": 10, "marital-status": "Never-married", "occupation": "Sales", "relationship": "Not-in-family", "race": "White", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 38, "n...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
11,797
{ "age": 32, "workclass": "Private", "fnlwgt": 264554, "education": "Some-college", "education-num": 10, "marital-status": "Married-civ-spouse", "occupation": "Tech-support", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, ...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
40,892
{ "age": 46, "workclass": "Private", "fnlwgt": 191204, "education": "Assoc-voc", "education-num": 11, "marital-status": "Never-married", "occupation": "Exec-managerial", "relationship": "Own-child", "race": "White", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, ...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
26,763
{ "age": 42, "workclass": "Self-emp-inc", "fnlwgt": 130126, "education": "Bachelors", "education-num": 13, "marital-status": "Married-civ-spouse", "occupation": "Prof-specialty", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 1977, "hours-per-week...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
6,353
{ "age": 37, "workclass": "Private", "fnlwgt": 318168, "education": "HS-grad", "education-num": 9, "marital-status": "Never-married", "occupation": "Machine-op-inspct", "relationship": "Not-in-family", "race": "Black", "sex": "Male", "capital-gain": 1055, "capital-loss": 0, "hours-per-week": 2...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
39,467
{ "age": 24, "workclass": "Private", "fnlwgt": 62952, "education": "HS-grad", "education-num": 9, "marital-status": "Never-married", "occupation": "Craft-repair", "relationship": "Own-child", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "native-...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
25,490
{ "age": 22, "workclass": "Private", "fnlwgt": 318915, "education": "HS-grad", "education-num": 9, "marital-status": "Never-married", "occupation": "Other-service", "relationship": "Unmarried", "race": "White", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "nat...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
8,276
{ "age": 48, "workclass": "Private", "fnlwgt": 166863, "education": "Masters", "education-num": 14, "marital-status": "Married-civ-spouse", "occupation": "Exec-managerial", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, ...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
9,743
{ "age": 38, "workclass": "Private", "fnlwgt": 35890, "education": "HS-grad", "education-num": 9, "marital-status": "Married-civ-spouse", "occupation": "Transport-moving", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
24,699
{ "age": 31, "workclass": "Private", "fnlwgt": 369825, "education": "Bachelors", "education-num": 13, "marital-status": "Never-married", "occupation": "Sales", "relationship": "Not-in-family", "race": "White", "sex": "Male", "capital-gain": 4101, "capital-loss": 0, "hours-per-week": 50, "nat...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
24,799
{ "age": 29, "workclass": "Private", "fnlwgt": 229729, "education": "HS-grad", "education-num": 9, "marital-status": "Never-married", "occupation": "Transport-moving", "relationship": "Not-in-family", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, ...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
36,908
{ "age": 34, "workclass": "Private", "fnlwgt": 125279, "education": "HS-grad", "education-num": 9, "marital-status": "Married-civ-spouse", "occupation": "Transport-moving", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, ...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
42,736
{ "age": 29, "workclass": "Private", "fnlwgt": 202878, "education": "7th-8th", "education-num": 4, "marital-status": "Married-civ-spouse", "occupation": "Farming-fishing", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 2042, "hours-per-week": 40, ...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
1,028
{ "age": 27, "workclass": "Private", "fnlwgt": 216479, "education": "Bachelors", "education-num": 13, "marital-status": "Never-married", "occupation": "Sales", "relationship": "Not-in-family", "race": "White", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "nati...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
42,694
{ "age": 23, "workclass": "Private", "fnlwgt": 45713, "education": "Some-college", "education-num": 10, "marital-status": "Never-married", "occupation": "Craft-repair", "relationship": "Other-relative", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40,...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
6,882
{ "age": 29, "workclass": "Private", "fnlwgt": 132675, "education": "11th", "education-num": 7, "marital-status": "Separated", "occupation": "Other-service", "relationship": "Own-child", "race": "Black", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "native-cou...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
4,618
{ "age": 18, "workclass": "Local-gov", "fnlwgt": 28357, "education": "HS-grad", "education-num": 9, "marital-status": "Never-married", "occupation": "Adm-clerical", "relationship": "Own-child", "race": "White", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 40, "nat...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
20,015
{ "age": 18, "workclass": "Private", "fnlwgt": 148644, "education": "HS-grad", "education-num": 9, "marital-status": "Never-married", "occupation": "Sales", "relationship": "Own-child", "race": "White", "sex": "Female", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 28, "native-coun...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
9,857
{ "age": 57, "workclass": "Self-emp-not-inc", "fnlwgt": 200316, "education": "7th-8th", "education-num": 4, "marital-status": "Married-civ-spouse", "occupation": "Craft-repair", "relationship": "Husband", "race": "White", "sex": "Male", "capital-gain": 0, "capital-loss": 0, "hours-per-week": 5...
{ "value": "<=50K", "label": "less than or equal to 50K" }
{ "value": "<=50K", "label": "less than or equal to 50K" }
The model predicted the individual's income to be less than or equal to 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
[ "TwoLayerNN", "TabNN" ]
Adult Census
tabular
48,592
{ "age": 54, "workclass": "Private", "fnlwgt": 161691, "education": "Masters", "education-num": 14, "marital-status": "Divorced", "occupation": "Prof-specialty", "relationship": "Not-in-family", "race": "White", "sex": "Female", "capital-gain": 0, "capital-loss": 2559, "hours-per-week": 40, ...
{ "value": ">50K", "label": "greater than 50K" }
{ "value": ">50K", "label": "greater than 50K" }
The model predicted the individual's income to be greater than 50K. Which input features were most responsible for this prediction?
1
Which part of the input was most responsible for the model’s prediction?
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