uid int64 2 364k | orig_metric stringclasses 30
values | sklearn_metric stringclasses 9
values | dataset_name stringlengths 2 124 | dataset_description stringlengths 3 13k ⌀ | dataset_features stringlengths 41 3.57M | task_description stringlengths 627 762 | task_name stringlengths 2 124 | attribute_names listlengths 0 100k | categorical_indicator listlengths 0 100k | __index_level_0__ int64 0 3.8k |
|---|---|---|---|---|---|---|---|---|---|---|
362,347 | mean_absolute_error | mean_absolute_error | IEEE80211aa-GATS | Data shows the downlink goodput for unicast and multicast transmissions with different group sizes and network loads on an IEEE 802.11ac network simulated on NS-3. Different IEEE 802.11aa GATS are used. | {0: [0 - level_0 (numeric)],
1: [1 - multicast_stations (numeric)],
2: [2 - unicast_stations (numeric)],
3: [3 - index (numeric)],
4: [4 - avg_datarate_downlink (numeric)],
5: [5 - avg_datarate_uplink (numeric)],
6: [6 - delay_downlink_multicast (numeric)],
7: [7 - delay_downlink_unicast (numeric)],
8: [8 - del... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 34.0,
'NumberOfInstances': 2824.0,
'NumberOfInstancesWithMissingValues': 432.0,
'NumberOfMissingValues': 432.0,
'NumberOfNumericFeatures': 33.0,
'NumberOfSymbolicFeatures': 0.0,
... | IEEE80211aa-GATS | [
"level_0",
"multicast_stations",
"unicast_stations",
"index",
"avg_datarate_downlink",
"avg_datarate_uplink",
"delay_downlink_multicast",
"delay_downlink_unicast",
"delay_uplink",
"gd_downlink_unicast",
"gd_uplink",
"injected_multicast",
"injected_unicast_down",
"injected_unicast_up",
"m... | [
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,030 |
362,346 | mean_absolute_error | mean_absolute_error | IEEE80211aa-GATS | Data shows the downlink goodput for unicast and multicast transmissions with different group sizes and network loads on an IEEE 802.11ac network simulated on NS-3. Different IEEE 802.11aa GATS are used. | {0: [0 - level_0 (numeric)],
1: [1 - multicast_stations (numeric)],
2: [2 - unicast_stations (numeric)],
3: [3 - index (numeric)],
4: [4 - avg_datarate_downlink (numeric)],
5: [5 - avg_datarate_uplink (numeric)],
6: [6 - delay_downlink_multicast (numeric)],
7: [7 - delay_downlink_unicast (numeric)],
8: [8 - del... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 34.0,
'NumberOfInstances': 3782.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 33.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | IEEE80211aa-GATS | [
"level_0",
"multicast_stations",
"unicast_stations",
"index",
"avg_datarate_downlink",
"avg_datarate_uplink",
"delay_downlink_multicast",
"delay_downlink_unicast",
"delay_uplink",
"gd_downlink_unicast",
"gd_uplink",
"injected_multicast",
"injected_unicast_down",
"injected_unicast_up",
"m... | [
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false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,031 |
362,465 | mean_absolute_error | mean_absolute_error | wine_quality | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on numerical features" benchmark. Original description:
**Author**: Tobias Kuehn
**Source**: Unknown - 2009
**Please cite**:
1. Title: Wine Quality... | {0: [0 - fixed.acidity (numeric)],
1: [1 - volatile.acidity (numeric)],
2: [2 - citric.acid (numeric)],
3: [3 - residual.sugar (numeric)],
4: [4 - chlorides (numeric)],
5: [5 - free.sulfur.dioxide (numeric)],
6: [6 - total.sulfur.dioxide (numeric)],
7: [7 - density (numeric)],
8: [8 - pH (numeric)],
9: [9 - su... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 6497.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | wine_quality | [
"fixed.acidity",
"volatile.acidity",
"citric.acid",
"residual.sugar",
"chlorides",
"free.sulfur.dioxide",
"total.sulfur.dioxide",
"density",
"pH",
"sulphates",
"alcohol"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,032 |
362,549 | mean_absolute_error | mean_absolute_error | Diabetes(scikit-learn) | .. _diabetes_dataset:
Diabetes dataset
----------------
Ten baseline variables, age, sex, body mass index, average blood
pressure, and six blood serum measurements were obtained for each of n =
442 diabetes patients, as well as the response of interest, a
quantitative measure of disease progression one year after bas... | {0: [0 - age (numeric)],
1: [1 - sex (numeric)],
2: [2 - bmi (numeric)],
3: [3 - bp (numeric)],
4: [4 - s1 (numeric)],
5: [5 - s2 (numeric)],
6: [6 - s3 (numeric)],
7: [7 - s4 (numeric)],
8: [8 - s5 (numeric)],
9: [9 - s6 (numeric)],
10: [10 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 442.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Diabetes(scikit-learn) | [
"age",
"sex",
"bmi",
"bp",
"s1",
"s2",
"s3",
"s4",
"s5",
"s6"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,033 |
362,559 | mean_absolute_error | mean_absolute_error | Reading_Hydro_upstream | Upstream data from the twin Archimedes screw hydro-electric generator on the river Thames at Caversham weir, Reading, UK. | {0: [0 - timestamp (numeric)], 1: [1 - upstream (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | Reading_Hydro_upstream | [
"timestamp"
] | [
false
] | 1,034 |
362,344 | mean_absolute_error | mean_absolute_error | IEEE80211aa-GATS | Data shows the downlink goodput for unicast and multicast transmissions with different group sizes and network loads on an IEEE 802.11ac network simulated on NS-3. Different IEEE 802.11aa GATS are used. | {0: [0 - level_0 (numeric)],
1: [1 - multicast_stations (numeric)],
2: [2 - unicast_stations (numeric)],
3: [3 - index (numeric)],
4: [4 - avg_datarate_downlink (numeric)],
5: [5 - avg_datarate_uplink (numeric)],
6: [6 - delay_downlink_multicast (numeric)],
7: [7 - delay_downlink_unicast (numeric)],
8: [8 - del... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 34.0,
'NumberOfInstances': 5296.0,
'NumberOfInstancesWithMissingValues': 1224.0,
'NumberOfMissingValues': 1224.0,
'NumberOfNumericFeatures': 33.0,
'NumberOfSymbolicFeatures': 0.0... | IEEE80211aa-GATS | [
"level_0",
"multicast_stations",
"unicast_stations",
"index",
"avg_datarate_downlink",
"avg_datarate_uplink",
"delay_downlink_multicast",
"delay_downlink_unicast",
"delay_uplink",
"gd_downlink_unicast",
"gd_uplink",
"injected_multicast",
"injected_unicast_down",
"injected_unicast_up",
"m... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,035 |
362,473 | mean_absolute_error | mean_absolute_error | wine_quality | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on numerical features" benchmark. Original description:
**Author**: Tobias Kuehn
**Source**: Unknown - 2009
**Please cite**:
1. Title: Wine Quality... | {0: [0 - fixed.acidity (numeric)],
1: [1 - volatile.acidity (numeric)],
2: [2 - citric.acid (numeric)],
3: [3 - residual.sugar (numeric)],
4: [4 - chlorides (numeric)],
5: [5 - free.sulfur.dioxide (numeric)],
6: [6 - total.sulfur.dioxide (numeric)],
7: [7 - density (numeric)],
8: [8 - pH (numeric)],
9: [9 - su... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 6497.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | wine_quality | [
"fixed.acidity",
"volatile.acidity",
"citric.acid",
"residual.sugar",
"chlorides",
"free.sulfur.dioxide",
"total.sulfur.dioxide",
"density",
"pH",
"sulphates",
"alcohol"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,036 |
362,341 | mean_absolute_error | mean_absolute_error | IEEE80211aa-GATS | Data shows the downlink goodput for unicast and multicast transmissions with different group sizes and network loads on an IEEE 802.11ac network simulated on NS-3. Different IEEE 802.11aa GATS are used. | {0: [0 - level_0 (numeric)],
1: [1 - multicast_stations (numeric)],
2: [2 - unicast_stations (numeric)],
3: [3 - index (numeric)],
4: [4 - avg_datarate_downlink (numeric)],
5: [5 - avg_datarate_uplink (numeric)],
6: [6 - delay_downlink_multicast (numeric)],
7: [7 - delay_downlink_unicast (numeric)],
8: [8 - del... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 34.0,
'NumberOfInstances': 4046.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 33.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | IEEE80211aa-GATS | [
"level_0",
"multicast_stations",
"unicast_stations",
"index",
"avg_datarate_downlink",
"avg_datarate_uplink",
"delay_downlink_multicast",
"delay_downlink_unicast",
"delay_uplink",
"gd_downlink_unicast",
"gd_uplink",
"injected_multicast",
"injected_unicast_down",
"injected_unicast_up",
"m... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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false,
false,
false,
false,
false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,037 |
362,561 | mean_absolute_error | mean_absolute_error | Diabetes(scikit-learn) | .. _diabetes_dataset:
Diabetes dataset
----------------
Ten baseline variables, age, sex, body mass index, average blood
pressure, and six blood serum measurements were obtained for each of n =
442 diabetes patients, as well as the response of interest, a
quantitative measure of disease progression one year after bas... | {0: [0 - age (numeric)],
1: [1 - sex (numeric)],
2: [2 - bmi (numeric)],
3: [3 - bp (numeric)],
4: [4 - s1 (numeric)],
5: [5 - s2 (numeric)],
6: [6 - s3 (numeric)],
7: [7 - s4 (numeric)],
8: [8 - s5 (numeric)],
9: [9 - s6 (numeric)],
10: [10 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 442.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Diabetes(scikit-learn) | [
"age",
"sex",
"bmi",
"bp",
"s1",
"s2",
"s3",
"s4",
"s5",
"s6"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,038 |
359,928 | root_relative_squared_error | root_mean_squared_error | colleges | Regroups information for about 7800 different US colleges. Including geographical information, stats about the population attending and post graduation career earnings. | {0: [0 - UNITID (numeric)],
1: [1 - school_name (string)],
2: [2 - city (string)],
3: [3 - state (nominal)],
4: [4 - zip (nominal)],
5: [5 - school_webpage (string)],
6: [6 - latitude (numeric)],
7: [7 - longitude (numeric)],
8: [8 - admission_rate (numeric)],
9: [9 - sat_verbal_midrange (numeric)],
10: [10 -... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 6039.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 50.0,
'NumberOfInstances': 7063.0,
'NumberOfInstancesWithMissingValues': 7063.0,
'NumberOfMissingValues': 125494.0,
'NumberOfNumericFeatures': 33.0,
'NumberOfSymbolicFeatures'... | colleges | [
"school_name",
"city",
"state",
"zip",
"school_webpage",
"latitude",
"longitude",
"admission_rate",
"sat_verbal_midrange",
"sat_math_midrange",
"sat_writing_midrange",
"act_combined_midrange",
"act_english_midrange",
"act_math_midrange",
"act_writing_midrange",
"sat_total_average",
"... | [
false,
false,
true,
true,
false,
false,
false,
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false,
false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true,
true,
true,... | 1,039 |
362,574 | mean_absolute_error | mean_absolute_error | test_dsn | Churn happens when customers are leaving their current service provider and moving to another one. This is a big business problem because it is more expensive to acquire a new customer than to keep an existing one from leaving. | {0: [0 - customer_id (string)],
1: [1 - network_age (numeric)],
2: [2 - customer_tenure_in_month (numeric)],
3: [3 - total_spend_in_months_1_and_2_of_2017 (numeric)],
4: [4 - total_sms_spend (numeric)],
5: [5 - total_data_spend (numeric)],
6: [6 - total_data_consumption (numeric)],
7: [7 - total_unique_calls (nu... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 1400.0,
'NumberOfInstancesWithMissingValues': 198.0,
'NumberOfMissingValues': 321.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
... | test_dsn | [
"customer_id",
"network_age",
"customer_tenure_in_month",
"total_spend_in_months_1_and_2_of_2017",
"total_sms_spend",
"total_data_spend",
"total_data_consumption",
"total_unique_calls",
"total_onnet_spend_",
"total_offnet_spend",
"total_call_centre_complaint_calls",
"network_type_subscription_... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,040 |
362,488 | mean_absolute_error | mean_absolute_error | wine_quality | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on numerical features" benchmark. Original description:
**Author**: Tobias Kuehn
**Source**: Unknown - 2009
**Please cite**:
1. Title: Wine Quality
2... | {0: [0 - fixed.acidity (numeric)],
1: [1 - volatile.acidity (numeric)],
2: [2 - citric.acid (numeric)],
3: [3 - residual.sugar (numeric)],
4: [4 - chlorides (numeric)],
5: [5 - free.sulfur.dioxide (numeric)],
6: [6 - total.sulfur.dioxide (numeric)],
7: [7 - density (numeric)],
8: [8 - pH (numeric)],
9: [9 - su... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 6497.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | wine_quality | [
"fixed.acidity",
"volatile.acidity",
"citric.acid",
"residual.sugar",
"chlorides",
"free.sulfur.dioxide",
"total.sulfur.dioxide",
"density",
"pH",
"sulphates",
"alcohol"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,041 |
362,560 | mean_absolute_error | mean_absolute_error | Reading_Hydro_upstream | Upstream data from the twin Archimedes screw hydro-electric generator on the river Thames at Caversham weir, Reading, UK. | {0: [0 - timestamp (numeric)], 1: [1 - downstream (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | Reading_Hydro_upstream | [
"timestamp"
] | [
false
] | 1,042 |
362,547 | mean_absolute_error | mean_absolute_error | Test-Diabetes(scikit-learn) | .. _diabetes_dataset:
Diabetes dataset
----------------
Ten baseline variables, age, sex, body mass index, average blood
pressure, and six blood serum measurements were obtained for each of n =
442 diabetes patients, as well as the response of interest, a
quantitative measure of disease progression one year after bas... | {0: [0 - age (numeric)],
1: [1 - sex (numeric)],
2: [2 - bmi (numeric)],
3: [3 - bp (numeric)],
4: [4 - s1 (numeric)],
5: [5 - s2 (numeric)],
6: [6 - s3 (numeric)],
7: [7 - s4 (numeric)],
8: [8 - s5 (numeric)],
9: [9 - s6 (numeric)],
10: [10 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 442.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Test-Diabetes(scikit-learn) | [
"age",
"sex",
"bmi",
"bp",
"s1",
"s2",
"s3",
"s4",
"s5",
"s6"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,043 |
362,343 | mean_absolute_error | mean_absolute_error | IEEE80211aa-GATS | Data shows the downlink goodput for unicast and multicast transmissions with different group sizes and network loads on an IEEE 802.11ac network simulated on NS-3. Different IEEE 802.11aa GATS are used. | {0: [0 - level_0 (numeric)],
1: [1 - multicast_stations (numeric)],
2: [2 - unicast_stations (numeric)],
3: [3 - index (numeric)],
4: [4 - avg_datarate_downlink (numeric)],
5: [5 - avg_datarate_uplink (numeric)],
6: [6 - delay_downlink_multicast (numeric)],
7: [7 - delay_downlink_unicast (numeric)],
8: [8 - del... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 34.0,
'NumberOfInstances': 4322.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 33.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | IEEE80211aa-GATS | [
"level_0",
"multicast_stations",
"unicast_stations",
"index",
"avg_datarate_downlink",
"avg_datarate_uplink",
"delay_downlink_multicast",
"delay_downlink_unicast",
"delay_uplink",
"gd_downlink_unicast",
"gd_uplink",
"injected_multicast",
"injected_unicast_down",
"injected_unicast_up",
"m... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,044 |
362,340 | mean_absolute_error | mean_absolute_error | IEEE80211aa-GATS | Data shows the downlink goodput for unicast and multicast transmissions with different group sizes and network loads on an IEEE 802.11ac network simulated on NS-3. Different IEEE 802.11aa GATS are used. | {0: [0 - level_0 (numeric)],
1: [1 - multicast_stations (numeric)],
2: [2 - unicast_stations (numeric)],
3: [3 - index (numeric)],
4: [4 - avg_datarate_downlink (numeric)],
5: [5 - avg_datarate_uplink (numeric)],
6: [6 - delay_downlink_multicast (numeric)],
7: [7 - delay_downlink_unicast (numeric)],
8: [8 - del... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 34.0,
'NumberOfInstances': 4046.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 33.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | IEEE80211aa-GATS | [
"level_0",
"multicast_stations",
"unicast_stations",
"index",
"avg_datarate_downlink",
"avg_datarate_uplink",
"delay_downlink_multicast",
"delay_downlink_unicast",
"delay_uplink",
"gd_downlink_unicast",
"gd_uplink",
"injected_multicast",
"injected_unicast_down",
"injected_unicast_up",
"m... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,045 |
362,512 | mean_absolute_error | mean_absolute_error | visualizing_soil | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark,
transformed in the same way. This dataset belongs to the "regression on categorical and
numerical features" benchmark. Original description:
**Author**:
**... | {0: [0 - northing (numeric)],
1: [1 - easting (numeric)],
2: [2 - resistivity (numeric)],
3: [3 - isns (nominal)],
4: [4 - track (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 8641.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | visualizing_soil | [
"northing",
"easting",
"resistivity",
"isns"
] | [
false,
false,
false,
true
] | 1,046 |
363,056 | mean_absolute_error | mean_absolute_error | cmc | This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey. The samples are married women who were either not pregnant or do not know if they were at the time of interview. The problem is to predict the current contraceptive method choice (no use, long-term methods, or short-term methods) o... | {0: [0 - Wifes_age (numeric)],
1: [1 - Number_of_children_ever_born (numeric)],
2: [2 - Wifes_education (numeric)],
3: [3 - Husbands_education (numeric)],
4: [4 - Wifes_religion (numeric)],
5: [5 - Wifes_now_working%3F (numeric)],
6: [6 - Husbands_occupation (numeric)],
7: [7 - Standard-of-living_index (numeric)... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 1473.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | cmc | [
"Wifes_age",
"Number_of_children_ever_born",
"Wifes_education",
"Husbands_education",
"Wifes_religion",
"Wifes_now_working%3F",
"Husbands_occupation",
"Standard-of-living_index",
"Media_exposure"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,048 |
363,058 | mean_absolute_error | mean_absolute_error | cmc | This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey. The samples are married women who were either not pregnant or do not know if they were at the time of interview. The problem is to predict the current contraceptive method choice (no use, long-term methods, or short-term methods) o... | {0: [0 - Wifes_age (numeric)],
1: [1 - Number_of_children_ever_born (numeric)],
2: [2 - Wifes_education (numeric)],
3: [3 - Husbands_education (numeric)],
4: [4 - Wifes_religion (numeric)],
5: [5 - Wifes_now_working%3F (numeric)],
6: [6 - Husbands_occupation (numeric)],
7: [7 - Standard-of-living_index (numeric)... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 1473.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | cmc | [
"Wifes_age",
"Number_of_children_ever_born",
"Wifes_education",
"Husbands_education",
"Wifes_religion",
"Wifes_now_working%3F",
"Husbands_occupation",
"Standard-of-living_index",
"Media_exposure"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,049 |
362,461 | mean_absolute_error | mean_absolute_error | cpu_act | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on numerical features" benchmark. Original description:
**Author**:
**Source**: Unknown -
**Please cite**:
The Computer Activity databases are a ... | {0: [0 - lread (numeric)],
1: [1 - lwrite (numeric)],
2: [2 - scall (numeric)],
3: [3 - sread (numeric)],
4: [4 - swrite (numeric)],
5: [5 - fork (numeric)],
6: [6 - exec (numeric)],
7: [7 - rchar (numeric)],
8: [8 - wchar (numeric)],
9: [9 - pgout (numeric)],
10: [10 - ppgout (numeric)],
11: [11 - pgfree (n... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 22.0,
'NumberOfInstances': 8192.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 22.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | cpu_act | [
"lread",
"lwrite",
"scall",
"sread",
"swrite",
"fork",
"exec",
"rchar",
"wchar",
"pgout",
"ppgout",
"pgfree",
"pgscan",
"atch",
"pgin",
"ppgin",
"pflt",
"vflt",
"runqsz",
"freemem",
"freeswap"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,050 |
362,556 | mean_absolute_error | mean_absolute_error | Intersectional-Bias-Assessment-(Testing-Data) | This synthetic dataset contains demographic and clinical data used to test the
performance of a trained classifier in predicting a diagnosis (of schizophrenia
or depression), and assess performance for intersectional bias.
This dataset is used in the tutorial 'An Intersectional Approach to Model
Construction and Evalu... | {0: [0 - Diagnosis (numeric)],
1: [1 - Sex (string)],
2: [2 - Race (string)],
3: [3 - Housing (string)],
4: [4 - Delay (string)],
5: [5 - Anhedonia (numeric)],
6: [6 - Dep_Mood (numeric)],
7: [7 - Sleep (numeric)],
8: [8 - Tired (numeric)],
9: [9 - Appetite (numeric)],
10: [10 - Rumination (numeric)],
11: [1... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 19.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 15.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Intersectional-Bias-Assessment-(Testing-Data) | [
"Sex",
"Race",
"Housing",
"Delay",
"Anhedonia",
"Dep_Mood",
"Sleep",
"Tired",
"Appetite",
"Rumination",
"Concentration",
"Psychomotor",
"Delusion",
"Suspicious",
"Withdrawal",
"Passive",
"Tension",
"Unusual_Thought"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,051 |
361,279 | root_mean_squared_error | root_mean_squared_error | yprop_4_1 | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on numerical features" benchmark.
Original link: https://openml.org/d/416
Original description:
**Author**:
**Source**: Unknown - Date unknown
**... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz9 (numeric)],
7: [7 - oz10 (numeric)],
8: [8 - oz11 (numeric)],
9: [9 - oz12 (numeric)],
10: [10 - oz13 (numeric)],
11: [11 - oz31 (numeric)],
12: [12 - ... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 43.0,
'NumberOfInstances': 8885.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 43.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | yprop_4_1 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz31",
"oz83",
"oz87",
"oz124",
"oz125",
"oz126",
"oz127",
"oz128",
"oz131",
"oz133",
"oz149",
"oz150",
"oz151",
"oz165",
"oz171",
"oz172",
"oz173",
"oz175",
"oz176",
"oz177"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 1,052 |
362,545 | mean_absolute_error | mean_absolute_error | VulNoneVul | Vulnerability Dataset For Binary Classification | {0: [0 - paramList (numeric)],
1: [1 - cyclomaticNum (numeric)],
2: [2 - loopNum (numeric)],
3: [3 - nestingDegree (numeric)],
4: [4 - SLOC (numeric)],
5: [5 - ALOC (numeric)],
6: [6 - localVars (numeric)],
7: [7 - localPtrVars (numeric)],
8: [8 - pointerArgs (numeric)],
9: [9 - callees (numeric)],
10: [10 - ... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 17.0,
'NumberOfInstances': 5692.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 17.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | VulNoneVul | [
"paramList",
"cyclomaticNum",
"loopNum",
"nestingDegree",
"SLOC",
"ALOC",
"localVars",
"localPtrVars",
"pointerArgs",
"callees",
"callers",
"height",
"conditions",
"cmps",
"jmps",
"ptrAssn"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,053 |
362,460 | mean_absolute_error | mean_absolute_error | cpu_act | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on numerical features" benchmark. Original description:
**Author**:
**Source**: Unknown -
**Please cite**:
The Computer Activity databases are a ... | {0: [0 - lread (numeric)],
1: [1 - lwrite (numeric)],
2: [2 - scall (numeric)],
3: [3 - sread (numeric)],
4: [4 - swrite (numeric)],
5: [5 - fork (numeric)],
6: [6 - exec (numeric)],
7: [7 - rchar (numeric)],
8: [8 - wchar (numeric)],
9: [9 - pgout (numeric)],
10: [10 - ppgout (numeric)],
11: [11 - pgfree (n... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 22.0,
'NumberOfInstances': 8192.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 22.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | cpu_act | [
"lread",
"lwrite",
"scall",
"sread",
"swrite",
"fork",
"exec",
"rchar",
"wchar",
"pgout",
"ppgout",
"pgfree",
"pgscan",
"atch",
"pgin",
"ppgin",
"pflt",
"vflt",
"runqsz",
"freemem",
"freeswap"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,054 |
363,057 | mean_absolute_error | mean_absolute_error | cmc | This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey. The samples are married women who were either not pregnant or do not know if they were at the time of interview. The problem is to predict the current contraceptive method choice (no use, long-term methods, or short-term methods) o... | {0: [0 - Wifes_age (numeric)],
1: [1 - Number_of_children_ever_born (numeric)],
2: [2 - Wifes_education (numeric)],
3: [3 - Husbands_education (numeric)],
4: [4 - Wifes_religion (numeric)],
5: [5 - Wifes_now_working%3F (numeric)],
6: [6 - Husbands_occupation (numeric)],
7: [7 - Standard-of-living_index (numeric)... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 1473.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 10.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | cmc | [
"Wifes_age",
"Number_of_children_ever_born",
"Wifes_education",
"Husbands_education",
"Wifes_religion",
"Wifes_now_working%3F",
"Husbands_occupation",
"Standard-of-living_index",
"Media_exposure"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,055 |
363,100 | mean_absolute_error | mean_absolute_error | Student-Scores | tbd | {0: [0 - Student_ID (numeric)],
1: [1 - Test_1 (numeric)],
2: [2 - Test_2 (numeric)],
3: [3 - Test_3 (numeric)],
4: [4 - Test_4 (numeric)],
5: [5 - Test_5 (numeric)],
6: [6 - Test_6 (numeric)],
7: [7 - Test_7 (numeric)],
8: [8 - Test_8 (numeric)],
9: [9 - Test_9 (numeric)],
10: [10 - Test_10 (numeric)],
11: ... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 13.0,
'NumberOfInstances': 56.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 13.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | Student-Scores | [
"Test_1",
"Test_2",
"Test_3",
"Test_4",
"Test_5",
"Test_6",
"Test_7",
"Test_8",
"Test_9",
"Test_10",
"Test_11",
"Test_12"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,056 |
211,871 | predictive_accuracy | accuracy_score | autoPrice | **Author**:
**Source**: Unknown -
**Please cite**:
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
All nominal attributes and instances with missing values are deleted.
Price treated as the class attribute.
As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric predictio... | {0: [0 - symboling (numeric)],
1: [1 - normalized-losses (numeric)],
2: [2 - wheel-base (numeric)],
3: [3 - length (numeric)],
4: [4 - width (numeric)],
5: [5 - height (numeric)],
6: [6 - curb-weight (numeric)],
7: [7 - engine-size (numeric)],
8: [8 - bore (numeric)],
9: [9 - stroke (numeric)],
10: [10 - comp... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 159.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 16.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | autoPrice | [
"symboling",
"normalized-losses",
"wheel-base",
"length",
"width",
"height",
"curb-weight",
"engine-size",
"bore",
"stroke",
"compression-ratio",
"horsepower",
"peak-rpm",
"city-mpg",
"highway-mpg"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,057 |
363,113 | mean_absolute_error | mean_absolute_error | SquareF | simple Math Function | {0: [0 - x (numeric)], 1: [1 - y (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | SquareF | [
"x"
] | [
false
] | 1,058 |
363,141 | mean_absolute_error | mean_absolute_error | Amphibian | amphibians | {0: [0 - ID (numeric)],
1: [1 - Motorway (string)],
2: [2 - SR (numeric)],
3: [3 - NR (numeric)],
4: [4 - TR (numeric)],
5: [5 - VR (numeric)],
6: [6 - SUR1 (numeric)],
7: [7 - SUR2 (numeric)],
8: [8 - SUR3 (numeric)],
9: [9 - UR (numeric)],
10: [10 - FR (numeric)],
11: [11 - OR (numeric)],
12: [12 - RR (nu... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 189.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 15.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Amphibian | [
"Motorway",
"SR",
"NR",
"TR",
"VR",
"SUR1",
"SUR2",
"SUR3",
"UR",
"FR",
"OR",
"RR",
"BR",
"MR",
"CR"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,059 |
363,120 | mean_absolute_error | mean_absolute_error | test | Myocardial infarction complications Database | {0: [0 - Feature1 (numeric)],
1: [1 - Feature2 (numeric)],
2: [2 - Feature4 (numeric)],
3: [3 - Feature5 (numeric)],
4: [4 - Target (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 30.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_m... | test | [
"Feature1",
"Feature2",
"Feature4",
"Feature5"
] | [
false,
false,
false,
false
] | 1,060 |
362,467 | mean_absolute_error | mean_absolute_error | cpu_act | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on numerical features" benchmark. Original description:
**Author**:
**Source**: Unknown -
**Please cite**:
The Computer Activity databases are a ... | {0: [0 - lread (numeric)],
1: [1 - lwrite (numeric)],
2: [2 - scall (numeric)],
3: [3 - sread (numeric)],
4: [4 - swrite (numeric)],
5: [5 - fork (numeric)],
6: [6 - exec (numeric)],
7: [7 - rchar (numeric)],
8: [8 - wchar (numeric)],
9: [9 - pgout (numeric)],
10: [10 - ppgout (numeric)],
11: [11 - pgfree (n... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 22.0,
'NumberOfInstances': 8192.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 22.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | cpu_act | [
"lread",
"lwrite",
"scall",
"sread",
"swrite",
"fork",
"exec",
"rchar",
"wchar",
"pgout",
"ppgout",
"pgfree",
"pgscan",
"atch",
"pgin",
"ppgin",
"pflt",
"vflt",
"runqsz",
"freemem",
"freeswap"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,061 |
362,548 | mean_absolute_error | mean_absolute_error | IEEE80211aa-GATS-NS3.35 | Data shows the downlink goodput for unicast and multicast transmissions with different group sizes and network loads on an IEEE 802.11ac network simulated on NS-3 v3.35. Different IEEE 802.11aa GATS are used. | {0: [0 - multicast_mode (string)],
1: [1 - multicast_stations (numeric)],
2: [2 - unicast_stations (numeric)],
3: [3 - injected_multicast (numeric)],
4: [4 - injected_unicast_down (numeric)],
5: [5 - injected_unicast_up_real (numeric)],
6: [6 - occupancy (numeric)],
7: [7 - occupancy_up (numeric)],
8: [8 - occu... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 24.0,
'NumberOfInstances': 2160.0,
'NumberOfInstancesWithMissingValues': 2160.0,
'NumberOfMissingValues': 2273.0,
'NumberOfNumericFeatures': 23.0,
'NumberOfSymbolicFeatures': 0.0... | IEEE80211aa-GATS-NS3.35 | [
"multicast_mode",
"multicast_stations",
"unicast_stations",
"injected_multicast",
"injected_unicast_down",
"injected_unicast_up_real",
"occupancy",
"occupancy_up",
"occupancy_down",
"avg_datarate_downlink",
"avg_datarate_uplink",
"delay_downlink_multicast",
"delay_downlink_unicast",
"delay... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,063 |
363,059 | mean_absolute_error | mean_absolute_error | cmc | This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey. The samples are married women who were either not pregnant or do not know if they were at the time of interview. The problem is to predict the current contraceptive method choice (no use, long-term methods, or short-term methods) o... | {0: [0 - Wifes_age (numeric)],
1: [1 - Number_of_children_ever_born (numeric)],
2: [2 - Wifes_education (nominal)],
3: [3 - Husbands_education (nominal)],
4: [4 - Wifes_religion (nominal)],
5: [5 - Wifes_now_working%3F (nominal)],
6: [6 - Husbands_occupation (nominal)],
7: [7 - Standard-of-living_index (nominal)... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 1473.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 7.0,
'cos... | cmc | [
"Wifes_age",
"Number_of_children_ever_born",
"Wifes_education",
"Husbands_education",
"Wifes_religion",
"Wifes_now_working%3F",
"Husbands_occupation",
"Standard-of-living_index",
"Media_exposure"
] | [
false,
false,
true,
true,
true,
true,
true,
true,
true
] | 1,064 |
362,546 | mean_absolute_error | mean_absolute_error | IEEE80211aa-GATS-NS3.35 | Data shows the downlink goodput for unicast and multicast transmissions with different group sizes and network loads on an IEEE 802.11ac network simulated on NS-3 v3.35. Different IEEE 802.11aa GATS are used. | {0: [0 - multicast_mode (string)],
1: [1 - multicast_stations (numeric)],
2: [2 - unicast_stations (numeric)],
3: [3 - injected_multicast (numeric)],
4: [4 - injected_unicast_down (numeric)],
5: [5 - injected_unicast_up_real (numeric)],
6: [6 - occupancy (numeric)],
7: [7 - occupancy_up (numeric)],
8: [8 - occu... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 24.0,
'NumberOfInstances': 2160.0,
'NumberOfInstancesWithMissingValues': 2160.0,
'NumberOfMissingValues': 2273.0,
'NumberOfNumericFeatures': 23.0,
'NumberOfSymbolicFeatures': 0.0... | IEEE80211aa-GATS-NS3.35 | [
"multicast_mode",
"multicast_stations",
"unicast_stations",
"injected_multicast",
"injected_unicast_down",
"injected_unicast_up_real",
"occupancy",
"occupancy_up",
"occupancy_down",
"avg_datarate_downlink",
"avg_datarate_uplink",
"delay_downlink_multicast",
"delay_downlink_unicast",
"delay... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,065 |
363,118 | mean_absolute_error | mean_absolute_error | test | Myocardial infarction complications Database | {0: [0 - Feature1 (numeric)],
1: [1 - Feature2 (numeric)],
2: [2 - Feature4 (numeric)],
3: [3 - Feature5 (numeric)],
4: [4 - Target (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 30.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_m... | test | [
"Feature1",
"Feature2",
"Feature4",
"Feature5"
] | [
false,
false,
false,
false
] | 1,066 |
363,119 | mean_absolute_error | mean_absolute_error | test | Myocardial infarction complications Database | {0: [0 - Feature1 (numeric)],
1: [1 - Feature2 (numeric)],
2: [2 - Feature3 (string)],
3: [3 - Feature4 (numeric)],
4: [4 - Feature5 (numeric)],
5: [5 - Target (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 30.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_m... | test | [
"Feature1",
"Feature2",
"Feature3",
"Feature4",
"Feature5"
] | [
false,
false,
false,
false,
false
] | 1,067 |
363,132 | mean_absolute_error | mean_absolute_error | 3D_Estimation_using_RSSI_of_WLAN_dataset | 3D Location Estimation using RSSI of WLAN dataset.The 3D Location Estimation Using RSSI of Wireless LAN challengeaims to develop an AI/ML-based localization algorithm that canaccurately estimate the position of a receiver based on RSS informationfrom surrounding radio transmitters including height information(enabling ... | {0: [0 - UnixTime (numeric)],
1: [1 - Latitude (numeric)],
2: [2 - Longitude (numeric)],
3: [3 - SSID (numeric)],
4: [4 - Frequency (numeric)],
5: [5 - Channel (numeric)],
6: [6 - RSSI (numeric)],
7: [7 - Receiver_Height (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 5760.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | 3D_Estimation_using_RSSI_of_WLAN_dataset | [
"UnixTime",
"Latitude",
"Longitude",
"SSID",
"Frequency",
"Channel",
"RSSI"
] | [
false,
false,
false,
false,
false,
false,
false
] | 1,068 |
363,196 | mean_absolute_error | mean_absolute_error | aids.id | Dataset aids.id from R package 'JM' | {0: [0 - time (numeric)],
1: [1 - status (numeric)],
2: [2 - CD4 (numeric)],
3: [3 - drug (nominal)],
4: [4 - gender (nominal)],
5: [5 - prevOI (nominal)],
6: [6 - AZT (nominal)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 467.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 4.0,
'cost_... | aids.id | [
"status",
"CD4",
"drug",
"gender",
"prevOI",
"AZT"
] | [
false,
false,
true,
true,
true,
true
] | 1,069 |
212,089 | predictive_accuracy | accuracy_score | Diabetes(scikit-learn) | .. _diabetes_dataset:
Diabetes dataset
----------------
Ten baseline variables, age, sex, body mass index, average blood
pressure, and six blood serum measurements were obtained for each of n =
442 diabetes patients, as well as the response of interest, a
quantitative measure of disease progression one year after bas... | {0: [0 - age (numeric)],
1: [1 - sex (numeric)],
2: [2 - bmi (numeric)],
3: [3 - bp (numeric)],
4: [4 - s1 (numeric)],
5: [5 - s2 (numeric)],
6: [6 - s3 (numeric)],
7: [7 - s4 (numeric)],
8: [8 - s5 (numeric)],
9: [9 - s6 (numeric)],
10: [10 - class (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 442.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | Diabetes(scikit-learn) | [
"age",
"sex",
"bmi",
"bp",
"s1",
"s2",
"s3",
"s4",
"s5",
"s6"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,070 |
363,201 | mean_absolute_error | mean_absolute_error | rdata | Dataset rdata from R package 'relsurv' | {0: [0 - time (numeric)],
1: [1 - status (numeric)],
2: [2 - age (numeric)],
3: [3 - sex (numeric)],
4: [4 - year (nominal)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 1040.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | rdata | [
"status",
"age",
"sex",
"year"
] | [
false,
false,
false,
true
] | 1,071 |
363,207 | mean_absolute_error | mean_absolute_error | wbc1 | Dataset wbc1 from R package 'dynpred' | {0: [0 - sokal (numeric)],
1: [1 - age (numeric)],
2: [2 - time (numeric)],
3: [3 - status (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 190.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | wbc1 | [
"sokal",
"age",
"status"
] | [
false,
false,
false
] | 1,073 |
363,204 | mean_absolute_error | mean_absolute_error | dataFTR | Dataset dataFTR from R package 'RISCA' | {0: [0 - time (numeric)],
1: [1 - status (numeric)],
2: [2 - ageR2cl (numeric)],
3: [3 - sexeR (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 2206.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | dataFTR | [
"status",
"ageR2cl",
"sexeR"
] | [
false,
false,
false
] | 1,074 |
363,091 | mean_absolute_error | mean_absolute_error | Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-10.5GHz(Urbinati) | Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-10.5GHz(Urbinati)
----------------
This dataset is part of a series of five different datasets
each one measured with a different microwave frequency: 9.0, 9.5, 10.0, 10.5, 11.0 GHz.
PAY ATTENTION: THE DATASET PR... | {0: [0 - s12 (numeric)],
1: [1 - s13 (numeric)],
2: [2 - s14 (numeric)],
3: [3 - s15 (numeric)],
4: [4 - s16 (numeric)],
5: [5 - s21 (numeric)],
6: [6 - s23 (numeric)],
7: [7 - s24 (numeric)],
8: [8 - s25 (numeric)],
9: [9 - s26 (numeric)],
10: [10 - s31 (numeric)],
11: [11 - s32 (numeric)],
12: [12 - s34 (... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 31.0,
'NumberOfInstances': 2400.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 31.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-10.5GHz(Urbinati) | [
"s12",
"s13",
"s14",
"s15",
"s16",
"s21",
"s23",
"s24",
"s25",
"s26",
"s31",
"s32",
"s34",
"s35",
"s36",
"s41",
"s42",
"s43",
"s45",
"s46",
"s51",
"s52",
"s53",
"s54",
"s56",
"s61",
"s62",
"s63",
"s64",
"s65"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,075 |
363,205 | mean_absolute_error | mean_absolute_error | dataSTR | Dataset dataSTR from R package 'RISCA' | {0: [0 - time (numeric)],
1: [1 - status (numeric)],
2: [2 - ageR2cl (numeric)],
3: [3 - sexeR (numeric)],
4: [4 - ageD2cl (numeric)],
5: [5 - Tattente2cl (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 546.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | dataSTR | [
"status",
"ageR2cl",
"sexeR",
"ageD2cl",
"Tattente2cl"
] | [
false,
false,
false,
false,
false
] | 1,076 |
363,051 | mean_absolute_error | mean_absolute_error | cpu_act | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on numerical features" benchmark.
Original link: https://openml.org/d/197
Original description:
**Author**:
**Source**: Unknown -
**Please cite*... | {0: [0 - lread (numeric)],
1: [1 - lwrite (numeric)],
2: [2 - scall (numeric)],
3: [3 - sread (numeric)],
4: [4 - swrite (numeric)],
5: [5 - fork (numeric)],
6: [6 - exec (numeric)],
7: [7 - rchar (numeric)],
8: [8 - wchar (numeric)],
9: [9 - pgout (numeric)],
10: [10 - ppgout (numeric)],
11: [11 - pgfree (n... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 22.0,
'NumberOfInstances': 8192.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 22.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | cpu_act | [
"lread",
"lwrite",
"scall",
"sread",
"swrite",
"fork",
"exec",
"rchar",
"wchar",
"pgout",
"ppgout",
"pgfree",
"pgscan",
"atch",
"pgin",
"ppgin",
"pflt",
"vflt",
"runqsz",
"freemem",
"freeswap"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,077 |
363,200 | mean_absolute_error | mean_absolute_error | colrec | Dataset colrec from R package 'relsurv' | {0: [0 - sex (nominal)],
1: [1 - age (numeric)],
2: [2 - diag (numeric)],
3: [3 - time (numeric)],
4: [4 - status (numeric)],
5: [5 - stage (numeric)],
6: [6 - site (nominal)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 5578.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 2.0,
'cost... | colrec | [
"sex",
"age",
"diag",
"status",
"stage",
"site"
] | [
true,
false,
false,
false,
false,
true
] | 1,079 |
363,090 | mean_absolute_error | mean_absolute_error | Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-10.0GHz(Urbinati) | Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-10.0GHz(Urbinati)
----------------
This dataset is part of a series of five different datasets
each one measured with a different microwave frequency: 9.0, 9.5, 10.0, 10.5, 11.0 GHz.
PAY ATTENTION: THE DATASET PR... | {0: [0 - s12 (numeric)],
1: [1 - s13 (numeric)],
2: [2 - s14 (numeric)],
3: [3 - s15 (numeric)],
4: [4 - s16 (numeric)],
5: [5 - s21 (numeric)],
6: [6 - s23 (numeric)],
7: [7 - s24 (numeric)],
8: [8 - s25 (numeric)],
9: [9 - s26 (numeric)],
10: [10 - s31 (numeric)],
11: [11 - s32 (numeric)],
12: [12 - s34 (... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 31.0,
'NumberOfInstances': 2400.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 31.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-10.0GHz(Urbinati) | [
"s12",
"s13",
"s14",
"s15",
"s16",
"s21",
"s23",
"s24",
"s25",
"s26",
"s31",
"s32",
"s34",
"s35",
"s36",
"s41",
"s42",
"s43",
"s45",
"s46",
"s51",
"s52",
"s53",
"s54",
"s56",
"s61",
"s62",
"s63",
"s64",
"s65"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,080 |
361,092 | root_mean_squared_error | root_mean_squared_error | yprop_4_1 | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark,
transformed in the same way. This dataset belongs to the "regression on categorical and
numerical features" benchmark. Original description:
**Author**:
**... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz9 (numeric)],
7: [7 - oz10 (numeric)],
8: [8 - oz11 (numeric)],
9: [9 - oz12 (numeric)],
10: [10 - oz13 (numeric)],
11: [11 - oz31 (numeric)],
12: [12 - ... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 63.0,
'NumberOfInstances': 8885.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 43.0,
'NumberOfSymbolicFeatures': 20.0,
'c... | yprop_4_1 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz31",
"oz40",
"oz42",
"oz46",
"oz50",
"oz69",
"oz71",
"oz73",
"oz79",
"oz83",
"oz87",
"oz96",
"oz100",
"oz107",
"oz108",
"oz111",
"oz112",
"oz113",
"oz115",
"oz124",
"oz12... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
false,
false,
false,
false,
false,
... | 1,081 |
363,199 | mean_absolute_error | mean_absolute_error | support | Dataset support from Python package 'pycox' | {0: [0 - x0 (numeric)],
1: [1 - x1 (numeric)],
2: [2 - x2 (numeric)],
3: [3 - x3 (numeric)],
4: [4 - x4 (numeric)],
5: [5 - x5 (numeric)],
6: [6 - x6 (numeric)],
7: [7 - x7 (numeric)],
8: [8 - x8 (numeric)],
9: [9 - x9 (numeric)],
10: [10 - x10 (numeric)],
11: [11 - x11 (numeric)],
12: [12 - x12 (numeric)],... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 16.0,
'NumberOfInstances': 4000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 16.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | support | [
"x0",
"x1",
"x2",
"x3",
"x4",
"x5",
"x6",
"x7",
"x8",
"x9",
"x10",
"x11",
"x12",
"x13",
"status"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,082 |
362,417 | mean_absolute_error | mean_absolute_error | ames_housing | Predict sales prices of houses. The Ames Housing dataset was compiled by Dean De Cock for use in data science education. | {0: [0 - MS_SubClass (nominal)],
1: [1 - MS_Zoning (nominal)],
2: [2 - Lot_Frontage (numeric)],
3: [3 - Lot_Area (numeric)],
4: [4 - Street (nominal)],
5: [5 - Alley (nominal)],
6: [6 - Lot_Shape (nominal)],
7: [7 - Land_Contour (nominal)],
8: [8 - Utilities (nominal)],
9: [9 - Lot_Config (nominal)],
10: [10 ... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 81.0,
'NumberOfInstances': 2930.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 35.0,
'NumberOfSymbolicFeatures': 46.0,
'c... | ames_housing | [
"MS_SubClass",
"MS_Zoning",
"Lot_Frontage",
"Lot_Area",
"Street",
"Alley",
"Lot_Shape",
"Land_Contour",
"Utilities",
"Lot_Config",
"Land_Slope",
"Neighborhood",
"Condition_1",
"Condition_2",
"Bldg_Type",
"House_Style",
"Overall_Qual",
"Overall_Cond",
"Year_Built",
"Year_Remod_A... | [
true,
true,
false,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
true,
true,
true,
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true,
false,
true,
true,
true,
true,
true,
true,
true,
false,
true,
false,
false,
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tru... | 1,083 |
363,089 | mean_absolute_error | mean_absolute_error | Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-9.0GHz(Urbinati) | Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-9.0GHz(Urbinati)
----------------
This dataset is part of a series of five different datasets
each one measured with a different microwave frequency: 9.0, 9.5, 10.0, 10.5, 11.0 GHz.
PAY ATTENTION: THE DATASET PRE... | {0: [0 - s12 (numeric)],
1: [1 - s13 (numeric)],
2: [2 - s14 (numeric)],
3: [3 - s15 (numeric)],
4: [4 - s16 (numeric)],
5: [5 - s21 (numeric)],
6: [6 - s23 (numeric)],
7: [7 - s24 (numeric)],
8: [8 - s25 (numeric)],
9: [9 - s26 (numeric)],
10: [10 - s31 (numeric)],
11: [11 - s32 (numeric)],
12: [12 - s34 (... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 31.0,
'NumberOfInstances': 2400.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 31.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-9.0GHz(Urbinati) | [
"s12",
"s13",
"s14",
"s15",
"s16",
"s21",
"s23",
"s24",
"s25",
"s26",
"s31",
"s32",
"s34",
"s35",
"s36",
"s41",
"s42",
"s43",
"s45",
"s46",
"s51",
"s52",
"s53",
"s54",
"s56",
"s61",
"s62",
"s63",
"s64",
"s65"
] | [
false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,085 |
359,930 | root_mean_squared_error | root_mean_squared_error | quake | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
File README
-----------
smoothmeth A collection of the data sets used in the book "Smoothing
Methods in Statistics," by Jeffrey S. Simonoff,
Springer-Verlag, New York, 1996. Submitted by Jeff
Simonoff (jsimonoff@stern.nyu.edu).
This submission... | {0: [0 - col_1 (numeric)],
1: [1 - col_2 (numeric)],
2: [2 - col_3 (numeric)],
3: [3 - col_4 (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 2178.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | quake | [
"col_1",
"col_2",
"col_3"
] | [
false,
false,
false
] | 1,086 |
363,206 | mean_absolute_error | mean_absolute_error | ova | Dataset ova from R package 'dynpred' | {0: [0 - Karn (numeric)],
1: [1 - Broders (nominal)],
2: [2 - Ascites (nominal)],
3: [3 - Diam (nominal)],
4: [4 - time (numeric)],
5: [5 - status (numeric)],
6: [6 - FIGOIII (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 358.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 3.0,
'cost_... | ova | [
"Karn",
"Broders",
"Ascites",
"Diam",
"status",
"FIGOIII"
] | [
false,
true,
true,
true,
false,
false
] | 1,087 |
363,093 | mean_absolute_error | mean_absolute_error | Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-11.0GHz(Urbinati) | Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-11.0GHz(Urbinati)
----------------
This dataset is part of a series of five different datasets
each one measured with a different microwave frequency: 9.0, 9.5, 10.0, 10.5, 11.0 GHz.
PAY ATTENTION: THE DATASET PR... | {0: [0 - s12 (numeric)],
1: [1 - s13 (numeric)],
2: [2 - s14 (numeric)],
3: [3 - s15 (numeric)],
4: [4 - s16 (numeric)],
5: [5 - s21 (numeric)],
6: [6 - s23 (numeric)],
7: [7 - s24 (numeric)],
8: [8 - s25 (numeric)],
9: [9 - s26 (numeric)],
10: [10 - s31 (numeric)],
11: [11 - s32 (numeric)],
12: [12 - s34 (... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 31.0,
'NumberOfInstances': 2400.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 31.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-11.0GHz(Urbinati) | [
"s12",
"s13",
"s14",
"s15",
"s16",
"s21",
"s23",
"s24",
"s25",
"s26",
"s31",
"s32",
"s34",
"s35",
"s36",
"s41",
"s42",
"s43",
"s45",
"s46",
"s51",
"s52",
"s53",
"s54",
"s56",
"s61",
"s62",
"s63",
"s64",
"s65"
] | [
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,088 |
362,319 | mean_absolute_error | mean_absolute_error | MIP-2016-regression | null | {0: [0 - instance_id (string)],
1: [1 - repetition (numeric)],
2: [2 - probtype (numeric)],
3: [3 - n_vars (numeric)],
4: [4 - n_constr (numeric)],
5: [5 - n_nzcnt (numeric)],
6: [6 - nq_vars (numeric)],
7: [7 - nq_constr (numeric)],
8: [8 - nq_nzcnt (numeric)],
9: [9 - lp_avg (numeric)],
10: [10 - lp_l2_avg ... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 147.0,
'NumberOfInstances': 1090.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 145.0,
'NumberOfSymbolicFeatures': 1.0,
'... | MIP-2016-regression | [
"instance_id",
"repetition",
"probtype",
"n_vars",
"n_constr",
"n_nzcnt",
"nq_vars",
"nq_constr",
"nq_nzcnt",
"lp_avg",
"lp_l2_avg",
"lp_linf",
"lp_objval",
"num_b_variables",
"num_i_variables",
"num_c_variables",
"num_s_variables",
"num_n_variables",
"ratio_b_variables",
"rati... | [
false,
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false,
false,
false,
false,
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false,
false,
false,
f... | 1,089 |
363,092 | mean_absolute_error | mean_absolute_error | Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-9.5GHz(Urbinati) | Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-9.5GHz(Urbinati)
----------------
This dataset is part of a series of five different datasets
each one measured with a different microwave frequency: 9.0, 9.5, 10.0, 10.5, 11.0 GHz.
PAY ATTENTION: THE DATASET PRE... | {0: [0 - s12 (numeric)],
1: [1 - s13 (numeric)],
2: [2 - s14 (numeric)],
3: [3 - s15 (numeric)],
4: [4 - s16 (numeric)],
5: [5 - s21 (numeric)],
6: [6 - s23 (numeric)],
7: [7 - s24 (numeric)],
8: [8 - s25 (numeric)],
9: [9 - s26 (numeric)],
10: [10 - s31 (numeric)],
11: [11 - s32 (numeric)],
12: [12 - s34 (... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 31.0,
'NumberOfInstances': 2400.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 31.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-Machine-Learning-9.5GHz(Urbinati) | [
"s12",
"s13",
"s14",
"s15",
"s16",
"s21",
"s23",
"s24",
"s25",
"s26",
"s31",
"s32",
"s34",
"s35",
"s36",
"s41",
"s42",
"s43",
"s45",
"s46",
"s51",
"s52",
"s53",
"s54",
"s56",
"s61",
"s62",
"s63",
"s64",
"s65"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,090 |
363,208 | mean_absolute_error | mean_absolute_error | uis | Dataset uis from R package 'quantreg' | {0: [0 - AGE (numeric)],
1: [1 - BECK (numeric)],
2: [2 - HC (nominal)],
3: [3 - IV (numeric)],
4: [4 - NDT (numeric)],
5: [5 - RACE (numeric)],
6: [6 - TREAT (numeric)],
7: [7 - SITE (numeric)],
8: [8 - time (numeric)],
9: [9 - status (numeric)],
10: [10 - ND1 (numeric)],
11: [11 - ND2 (numeric)],
12: [12 ... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 575.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 13.0,
'NumberOfSymbolicFeatures': 1.0,
'cos... | uis | [
"AGE",
"BECK",
"HC",
"IV",
"NDT",
"RACE",
"TREAT",
"SITE",
"status",
"ND1",
"ND2",
"LNDT",
"IV3"
] | [
false,
false,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,091 |
363,209 | mean_absolute_error | mean_absolute_error | kidtran | Dataset kidtran from R package 'KMsurv' | {0: [0 - time (numeric)],
1: [1 - age (numeric)],
2: [2 - status (numeric)],
3: [3 - genderF (numeric)],
4: [4 - raceBlack (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 863.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | kidtran | [
"age",
"status",
"genderF",
"raceBlack"
] | [
false,
false,
false,
false
] | 1,092 |
363,215 | mean_absolute_error | mean_absolute_error | CarpenterFdaData | Dataset CarpenterFdaData from R package 'simPH' | {0: [0 - time (numeric)],
1: [1 - status (numeric)],
2: [2 - hcomm (numeric)],
3: [3 - hfloor (numeric)],
4: [4 - scomm (numeric)],
5: [5 - sfloor (numeric)],
6: [6 - prespart (numeric)],
7: [7 - demhsmaj (numeric)],
8: [8 - demsnmaj (numeric)],
9: [9 - orderent (numeric)],
10: [10 - stafcder (numeric)],
11:... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 27.0,
'NumberOfInstances': 408.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 27.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | CarpenterFdaData | [
"status",
"hcomm",
"hfloor",
"scomm",
"sfloor",
"prespart",
"demhsmaj",
"demsnmaj",
"orderent",
"stafcder",
"prevgenx",
"lethal",
"deathrt1",
"hosp01",
"hospdisc",
"hhosleng",
"acutediz",
"orphdum",
"mandiz01",
"femdiz01",
"peddiz01",
"natreg",
"natregsq",
"wpnoavg3",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,093 |
363,211 | mean_absolute_error | mean_absolute_error | channing | Dataset channing from R package 'KMsurv' | {0: [0 - ageentry (numeric)],
1: [1 - time (numeric)],
2: [2 - status (numeric)],
3: [3 - genderF (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 458.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | channing | [
"ageentry",
"status",
"genderF"
] | [
false,
false,
false
] | 1,094 |
363,216 | mean_absolute_error | mean_absolute_error | liver | Dataset liver from R package 'joineR' | {0: [0 - prothrombin (numeric)],
1: [1 - treatment (numeric)],
2: [2 - time (numeric)],
3: [3 - status (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 488.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | liver | [
"prothrombin",
"treatment",
"status"
] | [
false,
false,
false
] | 1,095 |
363,212 | mean_absolute_error | mean_absolute_error | e1684 | Dataset e1684 from R package 'smcure' | {0: [0 - TRT (numeric)],
1: [1 - time (numeric)],
2: [2 - status (numeric)],
3: [3 - AGE (numeric)],
4: [4 - SEX (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 284.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | e1684 | [
"TRT",
"status",
"AGE",
"SEX"
] | [
false,
false,
false,
false
] | 1,096 |
363,210 | mean_absolute_error | mean_absolute_error | std | Dataset std from R package 'KMsurv' | {0: [0 - race (nominal)],
1: [1 - marital (nominal)],
2: [2 - age (numeric)],
3: [3 - yschool (numeric)],
4: [4 - iinfct (nominal)],
5: [5 - npartner (numeric)],
6: [6 - os12m (numeric)],
7: [7 - os30d (numeric)],
8: [8 - rs12m (numeric)],
9: [9 - rs30d (numeric)],
10: [10 - abdpain (numeric)],
11: [11 - dis... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 23.0,
'NumberOfInstances': 877.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 19.0,
'NumberOfSymbolicFeatures': 4.0,
'cos... | std | [
"race",
"marital",
"age",
"yschool",
"iinfct",
"npartner",
"os12m",
"os30d",
"rs12m",
"rs30d",
"abdpain",
"discharge",
"dysuria",
"condom",
"itch",
"lesion",
"rash",
"lymph",
"vagina",
"dchexam",
"abnode",
"status"
] | [
true,
true,
false,
false,
true,
false,
false,
false,
false,
false,
false,
false,
false,
true,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,097 |
359,945 | root_mean_squared_error | root_mean_squared_error | us_crime | Ignores community name.**Author**:
**Source**: Unknown - 2009
**Please cite**:
Title: Communities and Crime
Abstract: Communities within the United States. The data combines socio-economic data from the 1990 US Census, law enforcement data from the 1990 US LEMAS survey, and crime data from the 1995 FBI UC... | {0: [0 - state (numeric)],
1: [1 - county (numeric)],
2: [2 - community (numeric)],
3: [3 - communityname (string)],
4: [4 - fold (numeric)],
5: [5 - population (numeric)],
6: [6 - householdsize (numeric)],
7: [7 - racepctblack (numeric)],
8: [8 - racePctWhite (numeric)],
9: [9 - racePctAsian (numeric)],
10: ... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 127.0,
'NumberOfInstances': 1994.0,
'NumberOfInstancesWithMissingValues': 1871.0,
'NumberOfMissingValues': 39202.0,
'NumberOfNumericFeatures': 127.0,
'NumberOfSymbolicFeatures': ... | us_crime | [
"state",
"county",
"community",
"fold",
"population",
"householdsize",
"racepctblack",
"racePctWhite",
"racePctAsian",
"racePctHisp",
"agePct12t21",
"agePct12t29",
"agePct16t24",
"agePct65up",
"numbUrban",
"pctUrban",
"medIncome",
"pctWWage",
"pctWFarmSelf",
"pctWInvInc",
"pc... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 1,098 |
363,214 | mean_absolute_error | mean_absolute_error | aids2 | Dataset aids2 from R package 'nnet' | {0: [0 - state (nominal)],
1: [1 - status (numeric)],
2: [2 - T.categ (nominal)],
3: [3 - age (numeric)],
4: [4 - time (numeric)],
5: [5 - sexF (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 2814.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 2.0,
'cost... | aids2 | [
"state",
"status",
"T.categ",
"age",
"sexF"
] | [
true,
false,
true,
false,
false
] | 1,099 |
363,228 | mean_absolute_error | mean_absolute_error | bladder0 | Dataset bladder0 from R package 'frailtyHL' | {0: [0 - Center (nominal)],
1: [1 - time (numeric)],
2: [2 - status (numeric)],
3: [3 - Chemo (numeric)],
4: [4 - Tustat (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 397.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 1.0,
'cost_... | bladder0 | [
"Center",
"status",
"Chemo",
"Tustat"
] | [
true,
false,
false,
false
] | 1,100 |
359,931 | root_mean_squared_error | root_mean_squared_error | sensory | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Data for the sensory evaluation experiment in Brien, C.J. and Payne,
R.W. (1996) Tiers, structure formulae and the analysis of complicated
experiments. submitted for publication.
The experiment involved two phases. In the field phase a viticultu... | {0: [0 - Occasion (nominal)],
1: [1 - Judges (nominal)],
2: [2 - Interval (nominal)],
3: [3 - Sittings (nominal)],
4: [4 - Position (nominal)],
5: [5 - Squares (nominal)],
6: [6 - Rows (nominal)],
7: [7 - Columns (nominal)],
8: [8 - Halfplot (nominal)],
9: [9 - Trellis (nominal)],
10: [10 - Method (nominal)],... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 6.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 576.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 11.0,
'cos... | sensory | [
"Occasion",
"Judges",
"Interval",
"Sittings",
"Position",
"Squares",
"Rows",
"Columns",
"Halfplot",
"Trellis",
"Method"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 1,101 |
233,209 | predictive_accuracy | accuracy_score | train | test | {0: [0 - PassengerId (numeric)],
1: [1 - Survived (numeric)],
2: [2 - Pclass (numeric)],
3: [3 - Name (string)],
4: [4 - Sex (string)],
5: [5 - Age (numeric)],
6: [6 - SibSp (numeric)],
7: [7 - Parch (numeric)],
8: [8 - Ticket (string)],
9: [9 - Fare (numeric)],
10: [10 - Cabin (string)],
11: [11 - Embarked ... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': nan,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 891.0,
'NumberOfInstancesWithMissingValues': 708.0,
'NumberOfMissingValues': 866.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 0.0,
'... | train | [
"PassengerId",
"Pclass",
"Name",
"Sex",
"Age",
"SibSp",
"Parch",
"Ticket",
"Fare",
"Cabin",
"Embarked"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,102 |
363,222 | mean_absolute_error | mean_absolute_error | veteran | Dataset veteran from R package 'survival' | {0: [0 - trt (nominal)],
1: [1 - celltype (nominal)],
2: [2 - time (numeric)],
3: [3 - status (numeric)],
4: [4 - karno (numeric)],
5: [5 - diagtime (numeric)],
6: [6 - age (numeric)],
7: [7 - prior (nominal)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 137.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 3.0,
'cost_... | veteran | [
"trt",
"celltype",
"status",
"karno",
"diagtime",
"age",
"prior"
] | [
true,
true,
false,
false,
false,
false,
true
] | 1,103 |
363,226 | mean_absolute_error | mean_absolute_error | patient | Dataset patient from R package 'pammtools' | {0: [0 - Year (nominal)],
1: [1 - AdmCatID (nominal)],
2: [2 - ApacheIIScore (numeric)],
3: [3 - BMI (numeric)],
4: [4 - DiagID2 (nominal)],
5: [5 - time (numeric)],
6: [6 - status (numeric)],
7: [7 - sexF (numeric)],
8: [8 - age (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 1985.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 3.0,
'cost... | patient | [
"Year",
"AdmCatID",
"ApacheIIScore",
"BMI",
"DiagID2",
"status",
"sexF",
"age"
] | [
true,
true,
false,
false,
true,
false,
false,
false
] | 1,104 |
363,218 | mean_absolute_error | mean_absolute_error | lung | Dataset lung from R package 'survival' | {0: [0 - inst (nominal)],
1: [1 - time (numeric)],
2: [2 - status (numeric)],
3: [3 - age (numeric)],
4: [4 - sex (nominal)],
5: [5 - ph.ecog (numeric)],
6: [6 - ph.karno (numeric)],
7: [7 - pat.karno (numeric)],
8: [8 - meal.cal (numeric)],
9: [9 - wt.loss (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 167.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 2.0,
'cost... | lung | [
"inst",
"status",
"age",
"sex",
"ph.ecog",
"ph.karno",
"pat.karno",
"meal.cal",
"wt.loss"
] | [
true,
false,
false,
true,
false,
false,
false,
false,
false
] | 1,105 |
233,179 | predictive_accuracy | accuracy_score | stock_fardamento02 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
valores de saida de fardamento com temperaturas, admissões e demissões | {0: [0 - qts (numeric)],
1: [1 - Material (numeric)],
2: [2 - Dia (nominal)],
3: [3 - pp (numeric)],
4: [4 - TEMP (numeric)],
5: [5 - adm (numeric)],
6: [6 - Dem (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 6277.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | stock_fardamento02 | [
"Material",
"Dia",
"pp",
"TEMP",
"adm",
"Dem"
] | [
false,
true,
false,
false,
false,
false
] | 1,106 |
363,219 | mean_absolute_error | mean_absolute_error | mgus | Dataset mgus from R package 'survival' | {0: [0 - age (numeric)],
1: [1 - sex (nominal)],
2: [2 - dxyr (numeric)],
3: [3 - alb (numeric)],
4: [4 - creat (numeric)],
5: [5 - hgb (numeric)],
6: [6 - mspike (numeric)],
7: [7 - time (numeric)],
8: [8 - status (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 176.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 1.0,
'cost_... | mgus | [
"age",
"sex",
"dxyr",
"alb",
"creat",
"hgb",
"mspike",
"status"
] | [
false,
true,
false,
false,
false,
false,
false,
false
] | 1,107 |
363,221 | mean_absolute_error | mean_absolute_error | nwtco | Dataset nwtco from R package 'survival' | {0: [0 - histol (numeric)],
1: [1 - stage (nominal)],
2: [2 - age (numeric)],
3: [3 - status (numeric)],
4: [4 - time (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 4028.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | nwtco | [
"histol",
"stage",
"age",
"status"
] | [
false,
true,
false,
false
] | 1,108 |
363,225 | mean_absolute_error | mean_absolute_error | whas | Dataset whas from R package 'mlr3proba' | {0: [0 - time (numeric)],
1: [1 - status (numeric)],
2: [2 - age (numeric)],
3: [3 - chf (numeric)],
4: [4 - cpk (numeric)],
5: [5 - lenstay (numeric)],
6: [6 - miord (numeric)],
7: [7 - mitype (nominal)],
8: [8 - sexF (numeric)],
9: [9 - sho (numeric)],
10: [10 - year (nominal)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 11.0,
'NumberOfInstances': 481.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': 2.0,
'cost... | whas | [
"status",
"age",
"chf",
"cpk",
"lenstay",
"miord",
"mitype",
"sexF",
"sho",
"year"
] | [
false,
false,
false,
false,
false,
false,
true,
false,
false,
true
] | 1,109 |
363,217 | mean_absolute_error | mean_absolute_error | flchain | Dataset flchain from R package 'survival' | {0: [0 - age (numeric)],
1: [1 - sex (nominal)],
2: [2 - sample.yr (numeric)],
3: [3 - kappa (numeric)],
4: [4 - lambda (numeric)],
5: [5 - flc.grp (nominal)],
6: [6 - mgus (nominal)],
7: [7 - time (numeric)],
8: [8 - status (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 4000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 3.0,
'cost... | flchain | [
"age",
"sex",
"sample.yr",
"kappa",
"lambda",
"flc.grp",
"mgus",
"status"
] | [
false,
true,
false,
false,
false,
true,
true,
false
] | 1,110 |
359,944 | root_mean_squared_error | root_mean_squared_error | abalone | Make target (age) numeric**Author**:
**Source**: Unknown -
**Please cite**:
1. Title of Database: Abalone data
2. Sources:
(a) Original owners of database:
Marine Resources Division
Marine Research Laboratories - Taroona
Department of Primary Industry and Fisheries, Tasmania
GPO Box 619F, Hob... | {0: [0 - Sex (nominal)],
1: [1 - Length (numeric)],
2: [2 - Diameter (numeric)],
3: [3 - Height (numeric)],
4: [4 - Whole_weight (numeric)],
5: [5 - Shucked_weight (numeric)],
6: [6 - Viscera_weight (numeric)],
7: [7 - Shell_weight (numeric)],
8: [8 - Class_number_of_rings (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 4177.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 1.0,
'cost... | abalone | [
"Sex",
"Length",
"Diameter",
"Height",
"Whole_weight",
"Shucked_weight",
"Viscera_weight",
"Shell_weight"
] | [
true,
false,
false,
false,
false,
false,
false,
false
] | 1,111 |
362,295 | mean_absolute_error | mean_absolute_error | colleges | Version with corrected feature types. 'PrivacySuppressed' are converted to None.
Regroups information for about 7800 different US colleges. Including geographical information, stats about the population attending and post graduation career earnings. | {0: [0 - UNITID (numeric)],
1: [1 - school_name (string)],
2: [2 - city (string)],
3: [3 - state (nominal)],
4: [4 - zip (nominal)],
5: [5 - school_webpage (string)],
6: [6 - latitude (numeric)],
7: [7 - longitude (numeric)],
8: [8 - admission_rate (numeric)],
9: [9 - sat_verbal_midrange (numeric)],
10: [10 -... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 47.0,
'NumberOfInstances': 7063.0,
'NumberOfInstancesWithMissingValues': 7063.0,
'NumberOfMissingValues': 104305.0,
'NumberOfNumericFeatures': 33.0,
'NumberOfSymbolicFeatures': 1... | colleges | [
"school_name",
"city",
"state",
"zip",
"school_webpage",
"latitude",
"longitude",
"admission_rate",
"sat_verbal_midrange",
"sat_math_midrange",
"sat_writing_midrange",
"act_combined_midrange",
"act_english_midrange",
"act_math_midrange",
"act_writing_midrange",
"sat_total_average",
"... | [
false,
false,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true,
true,
true,
true,
true,
true,
... | 1,112 |
363,224 | mean_absolute_error | mean_absolute_error | grace | Dataset grace from R package 'mlr3proba' | {0: [0 - time (numeric)],
1: [1 - status (numeric)],
2: [2 - age (numeric)],
3: [3 - los (numeric)],
4: [4 - revasc (numeric)],
5: [5 - revascdays (numeric)],
6: [6 - stchange (numeric)],
7: [7 - sysbp (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | grace | [
"status",
"age",
"los",
"revasc",
"revascdays",
"stchange",
"sysbp"
] | [
false,
false,
false,
false,
false,
false,
false
] | 1,113 |
359,950 | root_mean_squared_error | root_mean_squared_error | boston | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic
prices and the demand for clean air', J. Environ. Economics & Management,
vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics
...', Wiley, 1980. N... | {0: [0 - CRIM (numeric)],
1: [1 - ZN (numeric)],
2: [2 - INDUS (numeric)],
3: [3 - CHAS (nominal)],
4: [4 - NOX (numeric)],
5: [5 - RM (numeric)],
6: [6 - AGE (numeric)],
7: [7 - DIS (numeric)],
8: [8 - RAD (nominal)],
9: [9 - TAX (numeric)],
10: [10 - PTRATIO (numeric)],
11: [11 - B (numeric)],
12: [12 - L... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': 9.0,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 14.0,
'NumberOfInstances': 506.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 2.0,
'cos... | boston | [
"CRIM",
"ZN",
"INDUS",
"CHAS",
"NOX",
"RM",
"AGE",
"DIS",
"RAD",
"TAX",
"PTRATIO",
"B",
"LSTAT"
] | [
false,
false,
false,
true,
false,
false,
false,
false,
true,
false,
false,
false,
false
] | 1,114 |
363,197 | mean_absolute_error | mean_absolute_error | Cancer_Drug_Response | The dataset is obtained from Qiao Liu et al. (3). | {0: [0 - LASP1 (numeric)],
1: [1 - HOXA11 (numeric)],
2: [2 - CREBBP (numeric)],
3: [3 - ETV1 (numeric)],
4: [4 - GAS7 (numeric)],
5: [5 - CD79B (numeric)],
6: [6 - PAX7 (numeric)],
7: [7 - BTK (numeric)],
8: [8 - BRCA1 (numeric)],
9: [9 - WAS (numeric)],
10: [10 - WWTR1 (numeric)],
11: [11 - CD74 (numeric)]... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 698.0,
'NumberOfInstances': 475.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 698.0,
'NumberOfSymbolicFeatures': 0.0,
'c... | Cancer_Drug_Response | [
"LASP1",
"HOXA11",
"CREBBP",
"ETV1",
"GAS7",
"CD79B",
"PAX7",
"BTK",
"BRCA1",
"WAS",
"WWTR1",
"CD74",
"BIRC3",
"FAS",
"BCLAF1",
"ANK1",
"RABEP1",
"ZCCHC8",
"CUL3",
"FLT4",
"CDH1",
"CDH10",
"TNC",
"EPHA3",
"PREX2",
"TPR",
"GOPC",
"ROS1",
"TNFRSF17",
"ELN",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 1,115 |
363,227 | mean_absolute_error | mean_absolute_error | tumor | Dataset tumor from R package 'pammtools' | {0: [0 - status (numeric)],
1: [1 - charlson_score (nominal)],
2: [2 - age (numeric)],
3: [3 - transfusion (numeric)],
4: [4 - complications (numeric)],
5: [5 - metastases (numeric)],
6: [6 - resection (numeric)],
7: [7 - time (numeric)],
8: [8 - sexF (numeric)]} | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 776.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 8.0,
'NumberOfSymbolicFeatures': 1.0,
'cost_... | tumor | [
"status",
"charlson_score",
"age",
"transfusion",
"complications",
"metastases",
"resection",
"sexF"
] | [
false,
true,
false,
false,
false,
false,
false,
false
] | 1,116 |
363,259 | mean_absolute_error | mean_absolute_error | metafeatures | meta features with best model | {0: [0 - DatasetRatio (numeric)],
1: [1 - InverseDatasetRatio (numeric)],
2: [2 - KurtosisMax (numeric)],
3: [3 - KurtosisMean (numeric)],
4: [4 - KurtosisMin (numeric)],
5: [5 - KurtosisSTD (numeric)],
6: [6 - LogDatasetRatio (numeric)],
7: [7 - LogInverseDatasetRatio (numeric)],
8: [8 - LogNumberOfFeatures (n... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 32.0,
'NumberOfInstances': 75.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 32.0,
'NumberOfSymbolicFeatures': 0.0,
'cost... | metafeatures | [
"DatasetRatio",
"InverseDatasetRatio",
"KurtosisMax",
"KurtosisMean",
"KurtosisMin",
"KurtosisSTD",
"LogDatasetRatio",
"LogInverseDatasetRatio",
"LogNumberOfFeatures",
"LogNumberOfInstances",
"NumberOfCategoricalFeatures",
"NumberOfFeatures",
"NumberOfFeaturesWithMissingValues",
"NumberOfI... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,117 |
363,265 | mean_absolute_error | mean_absolute_error | GermanCreditData | German credit dataset, similar to original one, just age and gender are two different attributes. More info on the dataset here: https://archive.ics.uci.edu/dataset/144/statlog+german+credit+data | {0: [0 - Unnamed: 0 (numeric)],
1: [1 - acc_status (string)],
2: [2 - acc_duration (numeric)],
3: [3 - credit_history (string)],
4: [4 - purpose (string)],
5: [5 - credit_amount (numeric)],
6: [6 - savings_acc (string)],
7: [7 - employment_time (string)],
8: [8 - installment_rate (numeric)],
9: [9 - gender (st... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 22.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': 0.0,
'cos... | GermanCreditData | [
"Unnamed: 0",
"acc_status",
"acc_duration",
"credit_history",
"purpose",
"credit_amount",
"savings_acc",
"employment_time",
"installment_rate",
"gender",
"other_debtors",
"residence_time",
"property",
"age",
"installment_plans",
"housing",
"num_credits_at_bank",
"job",
"num_peopl... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,118 |
362,509 | mean_absolute_error | mean_absolute_error | yprop_4_1 | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark,
transformed in the same way. This dataset belongs to the "regression on categorical and
numerical features" benchmark. Original description:
**Author**:
**... | {0: [0 - oz1 (numeric)],
1: [1 - oz2 (numeric)],
2: [2 - oz3 (numeric)],
3: [3 - oz4 (numeric)],
4: [4 - oz5 (numeric)],
5: [5 - oz6 (numeric)],
6: [6 - oz9 (numeric)],
7: [7 - oz10 (numeric)],
8: [8 - oz11 (numeric)],
9: [9 - oz12 (numeric)],
10: [10 - oz13 (numeric)],
11: [11 - oz31 (numeric)],
12: [12 - ... | {'MajorityClassSize': nan,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': nan,
'NumberOfClasses': 0.0,
'NumberOfFeatures': 63.0,
'NumberOfInstances': 8885.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 43.0,
'NumberOfSymbolicFeatures': 20.0,
'c... | yprop_4_1 | [
"oz1",
"oz2",
"oz3",
"oz4",
"oz5",
"oz6",
"oz9",
"oz10",
"oz11",
"oz12",
"oz13",
"oz31",
"oz40",
"oz42",
"oz46",
"oz50",
"oz69",
"oz71",
"oz73",
"oz79",
"oz83",
"oz87",
"oz96",
"oz100",
"oz107",
"oz108",
"oz111",
"oz112",
"oz113",
"oz115",
"oz124",
"oz12... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true,
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363,281 | mean_absolute_error | mean_absolute_error | dataset_analcatdata_creditscore | Financial dataset for automl benchmark. Name = dataset_analcatdata_creditscore, target = application_accepted | {0: [0 - age (numeric)],
1: [1 - income_per_dependent (numeric)],
2: [2 - monthly_credit_card_exp (numeric)],
3: [3 - own_home (numeric)],
4: [4 - self_employed (numeric)],
5: [5 - derogatory_reports (numeric)],
6: [6 - application_accepted (numeric)]} | {'MajorityClassSize': nan,
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'NumberOfInstances': 100.0,
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'NumberOfSymbolicFeatures': 0.0,
'cost_... | dataset_analcatdata_creditscore | [
"age",
"income_per_dependent",
"monthly_credit_card_exp",
"own_home",
"self_employed",
"derogatory_reports"
] | [
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false,
false,
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] | 1,120 |
363,440 | root_mean_squared_error | root_mean_squared_error | pakistan_hunger_data | About Dataset
This dataset provides a comprehensive overview of various hunger-related metrics in Pakistan from 2020 to 2023. It includes critical indicators such as the percentage of the population living under poverty, malnutrition rates, food insecurity levels, access to clean water, food production index, and the r... | {0: [0 - City (string)],
1: [1 - Year (numeric)],
2: [2 - Population_Under_Poverty (numeric)],
3: [3 - Malnutrition_Rate (numeric)],
4: [4 - Food_Insecurity (numeric)],
5: [5 - Access_to_Clean_Water (numeric)],
6: [6 - Food_Production_Index (numeric)],
7: [7 - Children_Underweight (numeric)]} | {'MajorityClassSize': nan,
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'NumberOfClasses': 0.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_... | pakistan_hunger_data | [
"City",
"Year",
"Population_Under_Poverty",
"Malnutrition_Rate",
"Food_Insecurity",
"Access_to_Clean_Water",
"Food_Production_Index"
] | [
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] | 1,121 |
363,399 | root_mean_squared_error | root_mean_squared_error | QSAR_Bioconcentration_regression | the QSAR Bioconcentration Classes Dataset is a well-known dataset used in cheminformatics and environmental chemistry. It is available from the UCI Machine Learning Repository and is often used for classification and regression tasks related to predicting the bioconcentration factor (BCF) of chemical compounds.
Datase... | {0: [0 - CAS (string)],
1: [1 - SMILES (string)],
2: [2 - Set (string)],
3: [3 - nHM (numeric)],
4: [4 - piPC09 (numeric)],
5: [5 - PCD (numeric)],
6: [6 - X2Av (numeric)],
7: [7 - MLOGP (numeric)],
8: [8 - ON1V (numeric)],
9: [9 - N-072 (numeric)],
10: [10 - B02[C-N] (numeric)],
11: [11 - F04[C-O] (numeric)... | {'MajorityClassSize': nan,
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'NumberOfInstances': 779.0,
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'cos... | QSAR_Bioconcentration_regression | [
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] | 1,122 |
363,417 | root_mean_squared_error | root_mean_squared_error | heart_failure_clinical_records | This dataset contains the medical records of 299 patients who had heart failure, collected during their follow-up period, where each patient profile has 13 clinical features.
Additional Information
A detailed description of the dataset can be found in the Dataset section of the following paper:
Davide Chicco, Giuse... | {0: [0 - age (numeric)],
1: [1 - anaemia (numeric)],
2: [2 - creatinine_phosphokinase (numeric)],
3: [3 - diabetes (numeric)],
4: [4 - ejection_fraction (numeric)],
5: [5 - high_blood_pressure (numeric)],
6: [6 - platelets (numeric)],
7: [7 - serum_creatinine (numeric)],
8: [8 - serum_sodium (numeric)],
9: [9 ... | {'MajorityClassSize': nan,
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'NumberOfInstances': 299.0,
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'cos... | heart_failure_clinical_records | [
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"anaemia",
"creatinine_phosphokinase",
"diabetes",
"ejection_fraction",
"high_blood_pressure",
"serum_creatinine",
"serum_sodium",
"sex",
"smoking",
"time",
"death_event"
] | [
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false,
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false,
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false,
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false,
false,
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] | 1,123 |
363,397 | root_mean_squared_error | root_mean_squared_error | Phenotype_202 | Detailed sequencing statistics and phenotypes of 202 individuals, for upload to OpenML we defined the variable 'Survive_time' as the target variable.
Visit https://figshare.com/articles/dataset/Phenotypic_descriptives_of_202_yellow_drum_individuals/12317489/2?file=22706906 for more information
We drop all nan values f... | {0: [0 - Orignal_ID (numeric)],
1: [1 - Sequence_ID (string)],
2: [2 - Coverage_rate(%) (numeric)],
3: [3 - Clean_reads (numeric)],
4: [4 - Clean_bases (numeric)],
5: [5 - Mapped_reads (numeric)],
6: [6 - Mapped_bases (numeric)],
7: [7 - Mapping_rate(%) (numeric)],
8: [8 - Sequencing_depth (numeric)],
9: [9 - ... | {'MajorityClassSize': nan,
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'NumberOfFeatures': 48.0,
'NumberOfInstances': 180.0,
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'cos... | Phenotype_202 | [
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"Mapped_reads",
"Mapped_bases",
"Mapping_rate(%)",
"Sequencing_depth",
"Effective_depth",
"Weight",
"Total_Length",
"Standard_Length",
"Height",
"Sex",
"Live_death",
"C14",
"C15",
"C16",
"C17",
"C1... | [
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363,242 | mean_absolute_error | mean_absolute_error | mabbob_ela_as_2d_regression_DiagonalCMA | Algorithm performane prediction problem on 1120 2d MA-BBOB test problems using ELA features to learn which of five algorithms has the highest AUCC. | {0: [0 - ela_meta.lin_simple.adj_r2 (numeric)],
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3: [3 - ela_meta.lin_simple.coef.max (numeric)],
4: [4 - ela_meta.lin_simple.coef.max_by_min (numeric)],
5: [5 - ela_meta.lin_w_interact.adj_r2 (numeric)],
6: [6 - ela... | {'MajorityClassSize': nan,
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'NumberOfSymbolicFeatures': 0.0,
'co... | mabbob_ela_as_2d_regression_DiagonalCMA | [
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363,426 | root_mean_squared_error | root_mean_squared_error | biosses | This dataset comes from the paper:BIGBIO: A Framework for Data-Centric Biomedical Natural Language Processing
This is the abstract of the paper:
Training and evaluating language models increasingly requires the construction of
meta-datasets- diverse collections of curated data with clear provenance. Natural language p... | {0: [0 - id (string)],
1: [1 - document_id (string)],
2: [2 - text_1 (string)],
3: [3 - text_2 (string)],
4: [4 - label (string)]} | {'MajorityClassSize': 16.0,
'MaxNominalAttDistinctValues': nan,
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'NumberOfClasses': 20.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 100.0,
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'cos... | biosses | [
"id",
"document_id",
"text_1",
"text_2"
] | [
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false,
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363,248 | mean_absolute_error | mean_absolute_error | mabbob_ela_as_5d_regression_DiagonalCMA | Algorithm performane prediction problem on 1120 5d MA-BBOB test problems using ELA features to learn which of five algorithms has the highest AUCC. | {0: [0 - ela_meta.lin_simple.adj_r2 (numeric)],
1: [1 - ela_meta.lin_simple.intercept (numeric)],
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3: [3 - ela_meta.lin_simple.coef.max (numeric)],
4: [4 - ela_meta.lin_simple.coef.max_by_min (numeric)],
5: [5 - ela_meta.lin_w_interact.adj_r2 (numeric)],
6: [6 - ela... | {'MajorityClassSize': nan,
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'co... | mabbob_ela_as_5d_regression_DiagonalCMA | [
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363,452 | root_mean_squared_error | root_mean_squared_error | sleep-deprivation-and-cognitive-performance | Impact of sleep deprivation on cognition and reaction time
About Dataset
This dataset explores the effects of sleep deprivation on cognitive performance and emotional regulation, based on a 2024 study conducted in the Middle East. It includes 60 participants from diverse backgrounds, capturing data on sleep duration,... | {0: [0 - Participant_ID (string)],
1: [1 - Sleep_Hours (numeric)],
2: [2 - Sleep_Quality_Score (numeric)],
3: [3 - Daytime_Sleepiness (numeric)],
4: [4 - Stroop_Task_Reaction_Time (numeric)],
5: [5 - N_Back_Accuracy (numeric)],
6: [6 - Emotion_Regulation_Score (numeric)],
7: [7 - PVT_Reaction_Time (numeric)],
8... | {'MajorityClassSize': nan,
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'NumberOfInstances': 60.0,
'NumberOfInstancesWithMissingValues': 0.0,
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'cost... | sleep-deprivation-and-cognitive-performance | [
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"Sleep_Hours",
"Sleep_Quality_Score",
"Daytime_Sleepiness",
"Stroop_Task_Reaction_Time",
"N_Back_Accuracy",
"Emotion_Regulation_Score",
"PVT_Reaction_Time",
"Age",
"Gender",
"BMI",
"Caffeine_Intake",
"Physical_Activity_Level"
] | [
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363,436 | root_mean_squared_error | root_mean_squared_error | climate_change_dataset2020-2024 | About Dataset
This dataset, spanning from January 2020 to May 2024, contains a comprehensive collection of global climate data. It includes various climatic features measured on a monthly basis, providing insights into weather patterns, temperature changes, and environmental conditions over time.
Usefulness
Climate Re... | {0: [0 - Year (string)],
1: [1 - Month (string)],
2: [2 - Avg_Temp_degC (numeric)],
3: [3 - Max_Temp_degC (string)],
4: [4 - Min_Temp_degC (string)],
5: [5 - Precipitation_mm (string)],
6: [6 - Humidity_pct (string)],
7: [7 - Wind_Speed_m_per_s (string)],
8: [8 - Solar_Irradiance_W_per_m2 (string)],
9: [9 - Cl... | {'MajorityClassSize': nan,
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'NumberOfInstances': 45.0,
'NumberOfInstancesWithMissingValues': 37.0,
'NumberOfMissingValues': 82.0,
'NumberOfNumericFeatures': 4.0,
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'cos... | climate_change_dataset2020-2024 | [
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"Month",
"Avg_Temp_degC",
"Max_Temp_degC",
"Min_Temp_degC",
"Precipitation_mm",
"Humidity_pct",
"Wind_Speed_m_per_s",
"Solar_Irradiance_W_per_m2",
"Cloud_Cover_pct",
"CO2_Concentration_ppm",
"Latitude",
"Longitude",
"Altitude_m",
"Proximity_to_Water_km",
"Urbanization_Index",
... | [
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] | 1,129 |
361,097 | root_mean_squared_error | root_mean_squared_error | Mercedes_Benz_Greener_Manufacturing | Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark,
transformed in the same way. This dataset belongs to the "regression on categorical and
numerical features" benchmark. Original description:
Since the first a... | {0: [0 - X3 (nominal)],
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5: [5 - X13 (nominal)],
6: [6 - X14 (nominal)],
7: [7 - X15 (nominal)],
8: [8 - X16 (nominal)],
9: [9 - X17 (nominal)],
10: [10 - X18 (nominal)],
11: [11 - X19 (nominal)],
12: [12 - X20 (nom... | {'MajorityClassSize': nan,
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'NumberOfInstances': 4209.0,
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'... | Mercedes_Benz_Greener_Manufacturing | [
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363,245 | mean_absolute_error | mean_absolute_error | mabbob_ela_as_2d_regression_modde | Algorithm performane prediction problem on 1120 2d MA-BBOB test problems using ELA features to learn which of five algorithms has the highest AUCC. | {0: [0 - ela_meta.lin_simple.adj_r2 (numeric)],
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4: [4 - ela_meta.lin_simple.coef.max_by_min (numeric)],
5: [5 - ela_meta.lin_w_interact.adj_r2 (numeric)],
6: [6 - ela... | {'MajorityClassSize': nan,
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'co... | mabbob_ela_as_2d_regression_modde | [
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363,453 | root_mean_squared_error | root_mean_squared_error | social-media-impact-on-suicide-rates | Clicks, Likes, and Life: Exploring Social Media's Role in Suicide Rates
About Dataset
Impact of Social Media on Suicide Rates: Produced Results
Overview
This dataset explores the impact of social media usage on suicide rates, presenting an analysis based on social media platform data and WHO suicide rate statistics. I... | {0: [0 - year (numeric)],
1: [1 - sex (string)],
2: [2 - suicide_rate_change_since_2010 (numeric)],
3: [3 - twitter_user_count_change_since_2010 (numeric)],
4: [4 - facebook_user_count_change_since_2010 (numeric)]} | {'MajorityClassSize': nan,
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'cost_m... | social-media-impact-on-suicide-rates | [
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"facebook_user_count_change_since_2010"
] | [
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363,439 | root_mean_squared_error | root_mean_squared_error | temperature_emissions_environmental_trends_2000_2024 | null | {0: [0 - Year (numeric)],
1: [1 - Country (string)],
2: [2 - Avg_Temperature_degC (numeric)],
3: [3 - CO2_Emissions_tons_per_capita (numeric)],
4: [4 - Sea_Level_Rise_mm (numeric)],
5: [5 - Rainfall_mm (numeric)],
6: [6 - Population (numeric)],
7: [7 - Renewable_Energy_pct (numeric)],
8: [8 - Extreme_Weather_Ev... | {'MajorityClassSize': nan,
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'NumberOfFeatures': 10.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
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'NumberOfNumericFeatures': 9.0,
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'cos... | temperature_emissions_environmental_trends_2000_2024 | [
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"Country",
"Avg_Temperature_degC",
"CO2_Emissions_tons_per_capita",
"Sea_Level_Rise_mm",
"Rainfall_mm",
"Population",
"Renewable_Energy_pct",
"Extreme_Weather_Events"
] | [
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] | 1,134 |
363,463 | mean_absolute_error | mean_absolute_error | football-player-position | Multi-class classification dataset with the goal to predict players positions based on features of the player like height, shots per match, ingame minutes per match, and others.
The target variables are:
0. Defender
1. Forward
2. Goalkeeper
3. Midfielder
The full list of features:
- Age
- Height
- Matches
- Minu... | {0: [0 - Age (numeric)],
1: [1 - Height (numeric)],
2: [2 - Matches (numeric)],
3: [3 - Minutes/Match (numeric)],
4: [4 - Goals/Match (numeric)],
5: [5 - Assists/Match (numeric)],
6: [6 - Yellow Cards/Match (numeric)],
7: [7 - Red Cards/Match (numeric)],
8: [8 - Shots/Match (numeric)],
9: [9 - Pass Success Rat... | {'MajorityClassSize': nan,
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'NumberOfClasses': 0.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 3611.0,
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'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | football-player-position | [
"Age",
"Height",
"Matches",
"Minutes/Match",
"Goals/Match",
"Assists/Match",
"Yellow Cards/Match",
"Red Cards/Match",
"Shots/Match",
"Pass Success Rate",
"Aerials Won"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 1,135 |
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