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 |
|---|---|---|---|---|---|---|---|---|---|---|
362,950 | predictive_accuracy | accuracy_score | connect-4_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset connect-4 (40668) with
seed=2
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: in... | {0: [0 - a1 (nominal)],
1: [1 - a2 (nominal)],
2: [2 - a3 (nominal)],
3: [3 - a4 (nominal)],
4: [4 - a5 (nominal)],
5: [5 - a6 (nominal)],
6: [6 - b1 (nominal)],
7: [7 - b2 (nominal)],
8: [8 - b3 (nominal)],
9: [9 - b4 (nominal)],
10: [10 - b5 (nominal)],
11: [11 - b6 (nominal)],
12: [12 - c1 (nominal)],
1... | {'MajorityClassSize': 1317.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 191.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 43.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 43.0,... | connect-4_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"a1",
"a2",
"a3",
"a4",
"a5",
"a6",
"b1",
"b2",
"b3",
"b4",
"b5",
"b6",
"c1",
"c2",
"c3",
"c4",
"c5",
"c6",
"d1",
"d2",
"d3",
"d4",
"d5",
"d6",
"e1",
"e2",
"e3",
"e4",
"e5",
"e6",
"f1",
"f2",
"f3",
"f4",
"f5",
"f6",
"g1",
"g2",
"g3",
"g4"... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true... | 3,359 |
362,940 | predictive_accuracy | accuracy_score | Satellite_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Satellite (40900) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: in... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 1971.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 29.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 37.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 36.0,
'NumberOfSymbolicFeatures': 1.0,
... | Satellite_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27",
"V28",
"V29",
"V30",
"V31",
"V32",
"V33",
"V34",
"V35",
"V36"
] | [
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... | 3,360 |
362,941 | predictive_accuracy | accuracy_score | Satellite_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Satellite (40900) with
seed=2
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: in... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 1971.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 29.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 37.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 36.0,
'NumberOfSymbolicFeatures': 1.0,
... | Satellite_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27",
"V28",
"V29",
"V30",
"V31",
"V32",
"V33",
"V34",
"V35",
"V36"
] | [
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... | 3,361 |
362,939 | predictive_accuracy | accuracy_score | Satellite_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Satellite (40900) with
seed=1
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: in... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 1971.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 29.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 37.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 36.0,
'NumberOfSymbolicFeatures': 1.0,
... | Satellite_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27",
"V28",
"V29",
"V30",
"V31",
"V32",
"V33",
"V34",
"V35",
"V36"
] | [
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... | 3,362 |
362,966 | predictive_accuracy | accuracy_score | jungle_chess_2pcs_raw_endgame_complete_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset jungle_chess_2pcs_raw_endgame_complete (41027) with
seed=4
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = ... | {0: [0 - white_piece0_strength (numeric)],
1: [1 - white_piece0_file (numeric)],
2: [2 - white_piece0_rank (numeric)],
3: [3 - black_piece0_strength (numeric)],
4: [4 - black_piece0_file (numeric)],
5: [5 - black_piece0_rank (numeric)],
6: [6 - class (nominal)]} | {'MajorityClassSize': 1029.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 194.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 1.0,
... | jungle_chess_2pcs_raw_endgame_complete_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"white_piece0_strength",
"white_piece0_file",
"white_piece0_rank",
"black_piece0_strength",
"black_piece0_file",
"black_piece0_rank"
] | [
false,
false,
false,
false,
false,
false
] | 3,364 |
362,805 | predictive_accuracy | accuracy_score | philippine_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset philippine (41145) with
seed=2
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: i... | {0: [0 - V10 (numeric)],
1: [1 - V12 (numeric)],
2: [2 - V13 (numeric)],
3: [3 - V18 (numeric)],
4: [4 - V20 (numeric)],
5: [5 - V23 (numeric)],
6: [6 - V24 (numeric)],
7: [7 - V29 (numeric)],
8: [8 - V31 (numeric)],
9: [9 - V35 (numeric)],
10: [10 - V42 (numeric)],
11: [11 - V46 (numeric)],
12: [12 - V47 (... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1... | philippine_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V10",
"V12",
"V13",
"V18",
"V20",
"V23",
"V24",
"V29",
"V31",
"V35",
"V42",
"V46",
"V47",
"V53",
"V55",
"V59",
"V61",
"V62",
"V63",
"V64",
"V66",
"V71",
"V73",
"V79",
"V83",
"V87",
"V89",
"V94",
"V97",
"V98",
"V101",
"V102",
"V114",
"V122",
"V124"... | [
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... | 3,365 |
362,942 | predictive_accuracy | accuracy_score | Satellite_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Satellite (40900) with
seed=4
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: in... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 1971.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 29.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 37.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 36.0,
'NumberOfSymbolicFeatures': 1.0,
... | Satellite_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27",
"V28",
"V29",
"V30",
"V31",
"V32",
"V33",
"V34",
"V35",
"V36"
] | [
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... | 3,366 |
362,964 | predictive_accuracy | accuracy_score | jungle_chess_2pcs_raw_endgame_complete_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset jungle_chess_2pcs_raw_endgame_complete (41027) with
seed=1
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = ... | {0: [0 - white_piece0_strength (numeric)],
1: [1 - white_piece0_file (numeric)],
2: [2 - white_piece0_rank (numeric)],
3: [3 - black_piece0_strength (numeric)],
4: [4 - black_piece0_file (numeric)],
5: [5 - black_piece0_rank (numeric)],
6: [6 - class (nominal)]} | {'MajorityClassSize': 1029.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 194.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 1.0,
... | jungle_chess_2pcs_raw_endgame_complete_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"white_piece0_strength",
"white_piece0_file",
"white_piece0_rank",
"black_piece0_strength",
"black_piece0_file",
"black_piece0_rank"
] | [
false,
false,
false,
false,
false,
false
] | 3,367 |
362,917 | predictive_accuracy | accuracy_score | GesturePhaseSegmentationProcessed_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset GesturePhaseSegmentationProcessed (4538) with
seed=4
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
... | {0: [0 - X1 (numeric)],
1: [1 - X2 (numeric)],
2: [2 - X3 (numeric)],
3: [3 - X4 (numeric)],
4: [4 - X5 (numeric)],
5: [5 - X6 (numeric)],
6: [6 - X7 (numeric)],
7: [7 - X8 (numeric)],
8: [8 - X9 (numeric)],
9: [9 - X10 (numeric)],
10: [10 - X11 (numeric)],
11: [11 - X12 (numeric)],
12: [12 - X13 (numeric)]... | {'MajorityClassSize': 598.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 202.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 33.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 32.0,
'NumberOfSymbolicFeatures': 1.0,
... | GesturePhaseSegmentationProcessed_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"X1",
"X2",
"X3",
"X4",
"X5",
"X6",
"X7",
"X8",
"X9",
"X10",
"X11",
"X12",
"X13",
"X14",
"X15",
"X16",
"X17",
"X18",
"X19",
"X20",
"X21",
"X22",
"X23",
"X24",
"X25",
"X26",
"X27",
"X28",
"X29",
"X30",
"X31",
"X32"
] | [
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
] | 3,368 |
362,954 | predictive_accuracy | accuracy_score | Amazon_employee_access_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Amazon_employee_access (4135) with
seed=1
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
ncla... | {0: [0 - RESOURCE (nominal)],
1: [1 - MGR_ID (nominal)],
2: [2 - ROLE_ROLLUP_1 (nominal)],
3: [3 - ROLE_ROLLUP_2 (nominal)],
4: [4 - ROLE_DEPTNAME (nominal)],
5: [5 - ROLE_TITLE (nominal)],
6: [6 - ROLE_FAMILY_DESC (nominal)],
7: [7 - ROLE_FAMILY (nominal)],
8: [8 - ROLE_CODE (nominal)],
9: [9 - target (nomina... | {'MajorityClassSize': 1884.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 116.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 10.0,... | Amazon_employee_access_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"RESOURCE",
"MGR_ID",
"ROLE_ROLLUP_1",
"ROLE_ROLLUP_2",
"ROLE_DEPTNAME",
"ROLE_TITLE",
"ROLE_FAMILY_DESC",
"ROLE_FAMILY",
"ROLE_CODE"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,369 |
362,956 | predictive_accuracy | accuracy_score | Amazon_employee_access_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Amazon_employee_access (4135) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
ncla... | {0: [0 - RESOURCE (nominal)],
1: [1 - MGR_ID (nominal)],
2: [2 - ROLE_ROLLUP_1 (nominal)],
3: [3 - ROLE_ROLLUP_2 (nominal)],
4: [4 - ROLE_DEPTNAME (nominal)],
5: [5 - ROLE_TITLE (nominal)],
6: [6 - ROLE_FAMILY_DESC (nominal)],
7: [7 - ROLE_FAMILY (nominal)],
8: [8 - ROLE_CODE (nominal)],
9: [9 - target (nomina... | {'MajorityClassSize': 1884.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 116.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 10.0,... | Amazon_employee_access_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"RESOURCE",
"MGR_ID",
"ROLE_ROLLUP_1",
"ROLE_ROLLUP_2",
"ROLE_DEPTNAME",
"ROLE_TITLE",
"ROLE_FAMILY_DESC",
"ROLE_FAMILY",
"ROLE_CODE"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,370 |
362,952 | predictive_accuracy | accuracy_score | connect-4_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset connect-4 (40668) with
seed=4
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: in... | {0: [0 - a1 (nominal)],
1: [1 - a2 (nominal)],
2: [2 - a3 (nominal)],
3: [3 - a4 (nominal)],
4: [4 - a5 (nominal)],
5: [5 - a6 (nominal)],
6: [6 - b1 (nominal)],
7: [7 - b2 (nominal)],
8: [8 - b3 (nominal)],
9: [9 - b4 (nominal)],
10: [10 - b5 (nominal)],
11: [11 - b6 (nominal)],
12: [12 - c1 (nominal)],
1... | {'MajorityClassSize': 1317.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 191.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 43.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 43.0,... | connect-4_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"a1",
"a2",
"a3",
"a4",
"a5",
"a6",
"b1",
"b2",
"b3",
"b4",
"b5",
"b6",
"c1",
"c2",
"c3",
"c4",
"c5",
"c6",
"d1",
"d2",
"d3",
"d4",
"d5",
"d6",
"e1",
"e2",
"e3",
"e4",
"e5",
"e6",
"f1",
"f2",
"f3",
"f4",
"f5",
"f6",
"g1",
"g2",
"g3",
"g4"... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true... | 3,371 |
362,953 | predictive_accuracy | accuracy_score | Amazon_employee_access_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Amazon_employee_access (4135) with
seed=0
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
ncla... | {0: [0 - RESOURCE (nominal)],
1: [1 - MGR_ID (nominal)],
2: [2 - ROLE_ROLLUP_1 (nominal)],
3: [3 - ROLE_ROLLUP_2 (nominal)],
4: [4 - ROLE_DEPTNAME (nominal)],
5: [5 - ROLE_TITLE (nominal)],
6: [6 - ROLE_FAMILY_DESC (nominal)],
7: [7 - ROLE_FAMILY (nominal)],
8: [8 - ROLE_CODE (nominal)],
9: [9 - target (nomina... | {'MajorityClassSize': 1884.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 116.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 10.0,... | Amazon_employee_access_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"RESOURCE",
"MGR_ID",
"ROLE_ROLLUP_1",
"ROLE_ROLLUP_2",
"ROLE_DEPTNAME",
"ROLE_TITLE",
"ROLE_FAMILY_DESC",
"ROLE_FAMILY",
"ROLE_CODE"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,373 |
362,916 | predictive_accuracy | accuracy_score | GesturePhaseSegmentationProcessed_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset GesturePhaseSegmentationProcessed (4538) with
seed=2
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
... | {0: [0 - X1 (numeric)],
1: [1 - X2 (numeric)],
2: [2 - X3 (numeric)],
3: [3 - X4 (numeric)],
4: [4 - X5 (numeric)],
5: [5 - X6 (numeric)],
6: [6 - X7 (numeric)],
7: [7 - X8 (numeric)],
8: [8 - X9 (numeric)],
9: [9 - X10 (numeric)],
10: [10 - X11 (numeric)],
11: [11 - X12 (numeric)],
12: [12 - X13 (numeric)]... | {'MajorityClassSize': 598.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 202.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 33.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 32.0,
'NumberOfSymbolicFeatures': 1.0,
... | GesturePhaseSegmentationProcessed_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"X1",
"X2",
"X3",
"X4",
"X5",
"X6",
"X7",
"X8",
"X9",
"X10",
"X11",
"X12",
"X13",
"X14",
"X15",
"X16",
"X17",
"X18",
"X19",
"X20",
"X21",
"X22",
"X23",
"X24",
"X25",
"X26",
"X27",
"X28",
"X29",
"X30",
"X31",
"X32"
] | [
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
] | 3,374 |
362,915 | predictive_accuracy | accuracy_score | GesturePhaseSegmentationProcessed_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset GesturePhaseSegmentationProcessed (4538) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
... | {0: [0 - X1 (numeric)],
1: [1 - X2 (numeric)],
2: [2 - X3 (numeric)],
3: [3 - X4 (numeric)],
4: [4 - X5 (numeric)],
5: [5 - X6 (numeric)],
6: [6 - X7 (numeric)],
7: [7 - X8 (numeric)],
8: [8 - X9 (numeric)],
9: [9 - X10 (numeric)],
10: [10 - X11 (numeric)],
11: [11 - X12 (numeric)],
12: [12 - X13 (numeric)]... | {'MajorityClassSize': 598.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 202.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 33.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 32.0,
'NumberOfSymbolicFeatures': 1.0,
... | GesturePhaseSegmentationProcessed_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"X1",
"X2",
"X3",
"X4",
"X5",
"X6",
"X7",
"X8",
"X9",
"X10",
"X11",
"X12",
"X13",
"X14",
"X15",
"X16",
"X17",
"X18",
"X19",
"X20",
"X21",
"X22",
"X23",
"X24",
"X25",
"X26",
"X27",
"X28",
"X29",
"X30",
"X31",
"X32"
] | [
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
] | 3,375 |
362,967 | predictive_accuracy | accuracy_score | jungle_chess_2pcs_raw_endgame_complete_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset jungle_chess_2pcs_raw_endgame_complete (41027) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = ... | {0: [0 - white_piece0_strength (numeric)],
1: [1 - white_piece0_file (numeric)],
2: [2 - white_piece0_rank (numeric)],
3: [3 - black_piece0_strength (numeric)],
4: [4 - black_piece0_file (numeric)],
5: [5 - black_piece0_rank (numeric)],
6: [6 - class (nominal)]} | {'MajorityClassSize': 1029.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 194.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 1.0,
... | jungle_chess_2pcs_raw_endgame_complete_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"white_piece0_strength",
"white_piece0_file",
"white_piece0_rank",
"black_piece0_strength",
"black_piece0_file",
"black_piece0_rank"
] | [
false,
false,
false,
false,
false,
false
] | 3,376 |
362,900 | predictive_accuracy | accuracy_score | Bioresponse_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Bioresponse (4134) with
seed=2
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: i... | {0: [0 - D69 (numeric)],
1: [1 - D94 (numeric)],
2: [2 - D101 (numeric)],
3: [3 - D135 (numeric)],
4: [4 - D155 (numeric)],
5: [5 - D171 (numeric)],
6: [6 - D179 (numeric)],
7: [7 - D182 (numeric)],
8: [8 - D184 (numeric)],
9: [9 - D186 (numeric)],
10: [10 - D256 (numeric)],
11: [11 - D318 (numeric)],
12: [... | {'MajorityClassSize': 1085.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 915.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | Bioresponse_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"D69",
"D94",
"D101",
"D135",
"D155",
"D171",
"D179",
"D182",
"D184",
"D186",
"D256",
"D318",
"D322",
"D342",
"D351",
"D374",
"D376",
"D384",
"D387",
"D439",
"D443",
"D459",
"D466",
"D502",
"D519",
"D550",
"D565",
"D572",
"D595",
"D600",
"D652",
"D672",
... | [
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... | 3,377 |
362,913 | predictive_accuracy | accuracy_score | GesturePhaseSegmentationProcessed_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset GesturePhaseSegmentationProcessed (4538) with
seed=0
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
... | {0: [0 - X1 (numeric)],
1: [1 - X2 (numeric)],
2: [2 - X3 (numeric)],
3: [3 - X4 (numeric)],
4: [4 - X5 (numeric)],
5: [5 - X6 (numeric)],
6: [6 - X7 (numeric)],
7: [7 - X8 (numeric)],
8: [8 - X9 (numeric)],
9: [9 - X10 (numeric)],
10: [10 - X11 (numeric)],
11: [11 - X12 (numeric)],
12: [12 - X13 (numeric)]... | {'MajorityClassSize': 598.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 202.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 33.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 32.0,
'NumberOfSymbolicFeatures': 1.0,
... | GesturePhaseSegmentationProcessed_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"X1",
"X2",
"X3",
"X4",
"X5",
"X6",
"X7",
"X8",
"X9",
"X10",
"X11",
"X12",
"X13",
"X14",
"X15",
"X16",
"X17",
"X18",
"X19",
"X20",
"X21",
"X22",
"X23",
"X24",
"X25",
"X26",
"X27",
"X28",
"X29",
"X30",
"X31",
"X32"
] | [
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
] | 3,378 |
362,970 | predictive_accuracy | accuracy_score | adult_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset adult (1590) with
seed=2
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int = 1... | {0: [0 - age (numeric)],
1: [1 - workclass (nominal)],
2: [2 - fnlwgt (numeric)],
3: [3 - education (nominal)],
4: [4 - education-num (numeric)],
5: [5 - marital-status (nominal)],
6: [6 - occupation (nominal)],
7: [7 - relationship (nominal)],
8: [8 - race (nominal)],
9: [9 - sex (nominal)],
10: [10 - capita... | {'MajorityClassSize': 1521.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 479.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 15.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 154.0,
'NumberOfMissingValues': 279.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 9... | adult_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"age",
"workclass",
"fnlwgt",
"education",
"education-num",
"marital-status",
"occupation",
"relationship",
"race",
"sex",
"capital-gain",
"capital-loss",
"hours-per-week",
"native-country"
] | [
false,
true,
false,
true,
false,
true,
true,
true,
true,
true,
false,
false,
false,
true
] | 3,379 |
362,973 | predictive_accuracy | accuracy_score | adult_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset adult (1590) with
seed=4
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int = 1... | {0: [0 - age (numeric)],
1: [1 - workclass (nominal)],
2: [2 - fnlwgt (numeric)],
3: [3 - education (nominal)],
4: [4 - education-num (numeric)],
5: [5 - marital-status (nominal)],
6: [6 - occupation (nominal)],
7: [7 - relationship (nominal)],
8: [8 - race (nominal)],
9: [9 - sex (nominal)],
10: [10 - capita... | {'MajorityClassSize': 1521.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 479.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 15.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 140.0,
'NumberOfMissingValues': 253.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 9... | adult_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"age",
"workclass",
"fnlwgt",
"education",
"education-num",
"marital-status",
"occupation",
"relationship",
"race",
"sex",
"capital-gain",
"capital-loss",
"hours-per-week",
"native-country"
] | [
false,
true,
false,
true,
false,
true,
true,
true,
true,
true,
false,
false,
false,
true
] | 3,380 |
362,914 | predictive_accuracy | accuracy_score | GesturePhaseSegmentationProcessed_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset GesturePhaseSegmentationProcessed (4538) with
seed=1
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
... | {0: [0 - X1 (numeric)],
1: [1 - X2 (numeric)],
2: [2 - X3 (numeric)],
3: [3 - X4 (numeric)],
4: [4 - X5 (numeric)],
5: [5 - X6 (numeric)],
6: [6 - X7 (numeric)],
7: [7 - X8 (numeric)],
8: [8 - X9 (numeric)],
9: [9 - X10 (numeric)],
10: [10 - X11 (numeric)],
11: [11 - X12 (numeric)],
12: [12 - X13 (numeric)]... | {'MajorityClassSize': 598.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 202.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 33.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 32.0,
'NumberOfSymbolicFeatures': 1.0,
... | GesturePhaseSegmentationProcessed_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"X1",
"X2",
"X3",
"X4",
"X5",
"X6",
"X7",
"X8",
"X9",
"X10",
"X11",
"X12",
"X13",
"X14",
"X15",
"X16",
"X17",
"X18",
"X19",
"X20",
"X21",
"X22",
"X23",
"X24",
"X25",
"X26",
"X27",
"X28",
"X29",
"X30",
"X31",
"X32"
] | [
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
] | 3,381 |
362,957 | predictive_accuracy | accuracy_score | Amazon_employee_access_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Amazon_employee_access (4135) with
seed=4
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
ncla... | {0: [0 - RESOURCE (nominal)],
1: [1 - MGR_ID (nominal)],
2: [2 - ROLE_ROLLUP_1 (nominal)],
3: [3 - ROLE_ROLLUP_2 (nominal)],
4: [4 - ROLE_DEPTNAME (nominal)],
5: [5 - ROLE_TITLE (nominal)],
6: [6 - ROLE_FAMILY_DESC (nominal)],
7: [7 - ROLE_FAMILY (nominal)],
8: [8 - ROLE_CODE (nominal)],
9: [9 - target (nomina... | {'MajorityClassSize': 1884.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 116.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 10.0,... | Amazon_employee_access_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"RESOURCE",
"MGR_ID",
"ROLE_ROLLUP_1",
"ROLE_ROLLUP_2",
"ROLE_DEPTNAME",
"ROLE_TITLE",
"ROLE_FAMILY_DESC",
"ROLE_FAMILY",
"ROLE_CODE"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,382 |
362,968 | predictive_accuracy | accuracy_score | adult_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset adult (1590) with
seed=0
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int = 1... | {0: [0 - age (numeric)],
1: [1 - workclass (nominal)],
2: [2 - fnlwgt (numeric)],
3: [3 - education (nominal)],
4: [4 - education-num (numeric)],
5: [5 - marital-status (nominal)],
6: [6 - occupation (nominal)],
7: [7 - relationship (nominal)],
8: [8 - race (nominal)],
9: [9 - sex (nominal)],
10: [10 - capita... | {'MajorityClassSize': 1521.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 479.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 15.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 135.0,
'NumberOfMissingValues': 242.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 9... | adult_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"age",
"workclass",
"fnlwgt",
"education",
"education-num",
"marital-status",
"occupation",
"relationship",
"race",
"sex",
"capital-gain",
"capital-loss",
"hours-per-week",
"native-country"
] | [
false,
true,
false,
true,
false,
true,
true,
true,
true,
true,
false,
false,
false,
true
] | 3,383 |
362,910 | predictive_accuracy | accuracy_score | first-order-theorem-proving_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset first-order-theorem-proving (1475) with
seed=2
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 835.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 159.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 52.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 51.0,
'NumberOfSymbolicFeatures': 1.0,
... | first-order-theorem-proving_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27",
"V28",
"V29",
"V30",
"V31",
"V32",
"V33",
"V34",
"V35",
"V36",
"... | [
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... | 3,384 |
362,946 | predictive_accuracy | accuracy_score | Fashion-MNIST_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Fashion-MNIST (40996) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max... | {0: [0 - pixel2 (numeric)],
1: [1 - pixel4 (numeric)],
2: [2 - pixel23 (numeric)],
3: [3 - pixel28 (numeric)],
4: [4 - pixel32 (numeric)],
5: [5 - pixel56 (numeric)],
6: [6 - pixel59 (numeric)],
7: [7 - pixel66 (numeric)],
8: [8 - pixel68 (numeric)],
9: [9 - pixel80 (numeric)],
10: [10 - pixel81 (numeric)],
... | {'MajorityClassSize': 200.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 200.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | Fashion-MNIST_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"pixel2",
"pixel4",
"pixel23",
"pixel28",
"pixel32",
"pixel56",
"pixel59",
"pixel66",
"pixel68",
"pixel80",
"pixel81",
"pixel102",
"pixel112",
"pixel124",
"pixel125",
"pixel141",
"pixel157",
"pixel163",
"pixel168",
"pixel174",
"pixel181",
"pixel186",
"pixel188",
"pixel1... | [
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... | 3,385 |
362,901 | predictive_accuracy | accuracy_score | Bioresponse_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Bioresponse (4134) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: i... | {0: [0 - D3 (numeric)],
1: [1 - D9 (numeric)],
2: [2 - D53 (numeric)],
3: [3 - D56 (numeric)],
4: [4 - D67 (numeric)],
5: [5 - D75 (numeric)],
6: [6 - D133 (numeric)],
7: [7 - D144 (numeric)],
8: [8 - D159 (numeric)],
9: [9 - D182 (numeric)],
10: [10 - D193 (numeric)],
11: [11 - D240 (numeric)],
12: [12 - D... | {'MajorityClassSize': 1085.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 915.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | Bioresponse_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"D3",
"D9",
"D53",
"D56",
"D67",
"D75",
"D133",
"D144",
"D159",
"D182",
"D193",
"D240",
"D271",
"D295",
"D302",
"D305",
"D330",
"D362",
"D385",
"D397",
"D398",
"D424",
"D434",
"D442",
"D448",
"D486",
"D503",
"D513",
"D514",
"D522",
"D542",
"D548",
"D56... | [
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... | 3,386 |
362,971 | predictive_accuracy | accuracy_score | adult_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset adult (1590) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int = 1... | {0: [0 - age (numeric)],
1: [1 - workclass (nominal)],
2: [2 - fnlwgt (numeric)],
3: [3 - education (nominal)],
4: [4 - education-num (numeric)],
5: [5 - marital-status (nominal)],
6: [6 - occupation (nominal)],
7: [7 - relationship (nominal)],
8: [8 - race (nominal)],
9: [9 - sex (nominal)],
10: [10 - capita... | {'MajorityClassSize': 1521.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 479.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 15.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 144.0,
'NumberOfMissingValues': 254.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 9... | adult_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"age",
"workclass",
"fnlwgt",
"education",
"education-num",
"marital-status",
"occupation",
"relationship",
"race",
"sex",
"capital-gain",
"capital-loss",
"hours-per-week",
"native-country"
] | [
false,
true,
false,
true,
false,
true,
true,
true,
true,
true,
false,
false,
false,
true
] | 3,387 |
362,908 | predictive_accuracy | accuracy_score | first-order-theorem-proving_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset first-order-theorem-proving (1475) with
seed=0
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 835.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 159.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 52.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 51.0,
'NumberOfSymbolicFeatures': 1.0,
... | first-order-theorem-proving_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27",
"V28",
"V29",
"V30",
"V31",
"V32",
"V33",
"V34",
"V35",
"V36",
"... | [
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... | 3,388 |
362,912 | predictive_accuracy | accuracy_score | first-order-theorem-proving_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset first-order-theorem-proving (1475) with
seed=4
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 835.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 159.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 52.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 51.0,
'NumberOfSymbolicFeatures': 1.0,
... | first-order-theorem-proving_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27",
"V28",
"V29",
"V30",
"V31",
"V32",
"V33",
"V34",
"V35",
"V36",
"... | [
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... | 3,389 |
362,909 | predictive_accuracy | accuracy_score | first-order-theorem-proving_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset first-order-theorem-proving (1475) with
seed=1
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 835.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 159.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 52.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 51.0,
'NumberOfSymbolicFeatures': 1.0,
... | first-order-theorem-proving_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27",
"V28",
"V29",
"V30",
"V31",
"V32",
"V33",
"V34",
"V35",
"V36",
"... | [
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... | 3,390 |
362,902 | predictive_accuracy | accuracy_score | Bioresponse_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Bioresponse (4134) with
seed=4
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: i... | {0: [0 - D48 (numeric)],
1: [1 - D62 (numeric)],
2: [2 - D103 (numeric)],
3: [3 - D137 (numeric)],
4: [4 - D139 (numeric)],
5: [5 - D156 (numeric)],
6: [6 - D203 (numeric)],
7: [7 - D227 (numeric)],
8: [8 - D230 (numeric)],
9: [9 - D238 (numeric)],
10: [10 - D287 (numeric)],
11: [11 - D296 (numeric)],
12: [... | {'MajorityClassSize': 1085.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 915.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | Bioresponse_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"D48",
"D62",
"D103",
"D137",
"D139",
"D156",
"D203",
"D227",
"D230",
"D238",
"D287",
"D296",
"D302",
"D305",
"D317",
"D337",
"D349",
"D358",
"D373",
"D382",
"D389",
"D392",
"D468",
"D478",
"D522",
"D574",
"D607",
"D631",
"D632",
"D636",
"D639",
"D646",
... | [
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... | 3,391 |
362,899 | predictive_accuracy | accuracy_score | Bioresponse_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Bioresponse (4134) with
seed=1
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: i... | {0: [0 - D35 (numeric)],
1: [1 - D47 (numeric)],
2: [2 - D59 (numeric)],
3: [3 - D70 (numeric)],
4: [4 - D96 (numeric)],
5: [5 - D107 (numeric)],
6: [6 - D110 (numeric)],
7: [7 - D146 (numeric)],
8: [8 - D160 (numeric)],
9: [9 - D202 (numeric)],
10: [10 - D212 (numeric)],
11: [11 - D213 (numeric)],
12: [12 ... | {'MajorityClassSize': 1085.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 915.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | Bioresponse_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"D35",
"D47",
"D59",
"D70",
"D96",
"D107",
"D110",
"D146",
"D160",
"D202",
"D212",
"D213",
"D230",
"D243",
"D263",
"D279",
"D349",
"D379",
"D420",
"D435",
"D451",
"D459",
"D462",
"D480",
"D483",
"D507",
"D510",
"D518",
"D526",
"D560",
"D562",
"D609",
"... | [
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... | 3,392 |
362,988 | predictive_accuracy | accuracy_score | shuttle_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset shuttle (40685) with
seed=1
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int ... | {0: [0 - A1 (numeric)],
1: [1 - A2 (numeric)],
2: [2 - A3 (numeric)],
3: [3 - A4 (numeric)],
4: [4 - A5 (numeric)],
5: [5 - A6 (numeric)],
6: [6 - A7 (numeric)],
7: [7 - A8 (numeric)],
8: [8 - A9 (numeric)],
9: [9 - class (nominal)]} | {'MajorityClassSize': 1572.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 2.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': 1.0,
'... | shuttle_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"A1",
"A2",
"A3",
"A4",
"A5",
"A6",
"A7",
"A8",
"A9"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,393 |
362,992 | predictive_accuracy | accuracy_score | shuttle_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset shuttle (40685) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int ... | {0: [0 - A1 (numeric)],
1: [1 - A2 (numeric)],
2: [2 - A3 (numeric)],
3: [3 - A4 (numeric)],
4: [4 - A5 (numeric)],
5: [5 - A6 (numeric)],
6: [6 - A7 (numeric)],
7: [7 - A8 (numeric)],
8: [8 - A9 (numeric)],
9: [9 - class (nominal)]} | {'MajorityClassSize': 1572.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 2.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': 1.0,
'... | shuttle_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"A1",
"A2",
"A3",
"A4",
"A5",
"A6",
"A7",
"A8",
"A9"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,394 |
362,944 | predictive_accuracy | accuracy_score | Fashion-MNIST_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Fashion-MNIST (40996) with
seed=1
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max... | {0: [0 - pixel15 (numeric)],
1: [1 - pixel20 (numeric)],
2: [2 - pixel25 (numeric)],
3: [3 - pixel31 (numeric)],
4: [4 - pixel43 (numeric)],
5: [5 - pixel46 (numeric)],
6: [6 - pixel48 (numeric)],
7: [7 - pixel61 (numeric)],
8: [8 - pixel68 (numeric)],
9: [9 - pixel86 (numeric)],
10: [10 - pixel87 (numeric)],... | {'MajorityClassSize': 200.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 200.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | Fashion-MNIST_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"pixel15",
"pixel20",
"pixel25",
"pixel31",
"pixel43",
"pixel46",
"pixel48",
"pixel61",
"pixel68",
"pixel86",
"pixel87",
"pixel89",
"pixel97",
"pixel100",
"pixel116",
"pixel120",
"pixel147",
"pixel166",
"pixel173",
"pixel180",
"pixel190",
"pixel191",
"pixel201",
"pixel2... | [
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... | 3,395 |
362,990 | predictive_accuracy | accuracy_score | shuttle_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset shuttle (40685) with
seed=0
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int ... | {0: [0 - A1 (numeric)],
1: [1 - A2 (numeric)],
2: [2 - A3 (numeric)],
3: [3 - A4 (numeric)],
4: [4 - A5 (numeric)],
5: [5 - A6 (numeric)],
6: [6 - A7 (numeric)],
7: [7 - A8 (numeric)],
8: [8 - A9 (numeric)],
9: [9 - class (nominal)]} | {'MajorityClassSize': 1572.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 2.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': 1.0,
'... | shuttle_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"A1",
"A2",
"A3",
"A4",
"A5",
"A6",
"A7",
"A8",
"A9"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,396 |
362,991 | predictive_accuracy | accuracy_score | shuttle_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset shuttle (40685) with
seed=4
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int ... | {0: [0 - A1 (numeric)],
1: [1 - A2 (numeric)],
2: [2 - A3 (numeric)],
3: [3 - A4 (numeric)],
4: [4 - A5 (numeric)],
5: [5 - A6 (numeric)],
6: [6 - A7 (numeric)],
7: [7 - A8 (numeric)],
8: [8 - A9 (numeric)],
9: [9 - class (nominal)]} | {'MajorityClassSize': 1572.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 2.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': 1.0,
'... | shuttle_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"A1",
"A2",
"A3",
"A4",
"A5",
"A6",
"A7",
"A8",
"A9"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,397 |
4,637 | predictive_accuracy | accuracy_score | anthracyclineTaxaneChemotherapy | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Data from the RSCTC 2010 Discovery Challenge. All datasets contain between 100 and 400 samples, characterized by values of 20,000 - 65,000 attributes. Samples are assigned to several (2-10) classes. All attributes are numeric and represent measure... | {0: [0 - Var1 (numeric)],
1: [1 - Var2 (numeric)],
2: [2 - Var3 (numeric)],
3: [3 - Var4 (numeric)],
4: [4 - Var5 (numeric)],
5: [5 - Var6 (numeric)],
6: [6 - Var7 (numeric)],
7: [7 - Var8 (numeric)],
8: [8 - Var9 (numeric)],
9: [9 - Var10 (numeric)],
10: [10 - Var11 (numeric)],
11: [11 - Var12 (numeric)],
... | {'MajorityClassSize': 95.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 64.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 61360.0,
'NumberOfInstances': 159.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 61359.0,
'NumberOfSymbolicFeatures': 1.... | anthracyclineTaxaneChemotherapy | [
"Var1",
"Var2",
"Var3",
"Var4",
"Var5",
"Var6",
"Var7",
"Var8",
"Var9",
"Var10",
"Var11",
"Var12",
"Var13",
"Var14",
"Var15",
"Var16",
"Var17",
"Var18",
"Var19",
"Var20",
"Var21",
"Var22",
"Var23",
"Var24",
"Var25",
"Var26",
"Var27",
"Var28",
"Var29",
"Var30... | [
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... | 3,398 |
362,989 | predictive_accuracy | accuracy_score | shuttle_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset shuttle (40685) with
seed=2
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int ... | {0: [0 - A1 (numeric)],
1: [1 - A2 (numeric)],
2: [2 - A3 (numeric)],
3: [3 - A4 (numeric)],
4: [4 - A5 (numeric)],
5: [5 - A6 (numeric)],
6: [6 - A7 (numeric)],
7: [7 - A8 (numeric)],
8: [8 - A9 (numeric)],
9: [9 - class (nominal)]} | {'MajorityClassSize': 1572.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 2.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 9.0,
'NumberOfSymbolicFeatures': 1.0,
'... | shuttle_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"A1",
"A2",
"A3",
"A4",
"A5",
"A6",
"A7",
"A8",
"A9"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,399 |
362,911 | predictive_accuracy | accuracy_score | first-order-theorem-proving_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset first-order-theorem-proving (1475) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 835.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 159.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 52.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 51.0,
'NumberOfSymbolicFeatures': 1.0,
... | first-order-theorem-proving_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27",
"V28",
"V29",
"V30",
"V31",
"V32",
"V33",
"V34",
"V35",
"V36",
"... | [
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... | 3,400 |
362,943 | predictive_accuracy | accuracy_score | Fashion-MNIST_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Fashion-MNIST (40996) with
seed=0
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max... | {0: [0 - pixel2 (numeric)],
1: [1 - pixel4 (numeric)],
2: [2 - pixel7 (numeric)],
3: [3 - pixel12 (numeric)],
4: [4 - pixel16 (numeric)],
5: [5 - pixel21 (numeric)],
6: [6 - pixel24 (numeric)],
7: [7 - pixel29 (numeric)],
8: [8 - pixel38 (numeric)],
9: [9 - pixel52 (numeric)],
10: [10 - pixel56 (numeric)],
1... | {'MajorityClassSize': 200.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 200.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | Fashion-MNIST_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"pixel2",
"pixel4",
"pixel7",
"pixel12",
"pixel16",
"pixel21",
"pixel24",
"pixel29",
"pixel38",
"pixel52",
"pixel56",
"pixel58",
"pixel65",
"pixel66",
"pixel91",
"pixel102",
"pixel122",
"pixel126",
"pixel177",
"pixel186",
"pixel189",
"pixel196",
"pixel206",
"pixel213",
... | [
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... | 3,401 |
362,972 | predictive_accuracy | accuracy_score | helena_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset helena (41169) with
seed=0
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int =... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 123.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 3.0,
'NumberOfClasses': 100.0,
'NumberOfFeatures': 28.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 27.0,
'NumberOfSymbolicFeatures': 1.0,
... | helena_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27"
] | [
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
] | 3,402 |
362,945 | predictive_accuracy | accuracy_score | Fashion-MNIST_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Fashion-MNIST (40996) with
seed=2
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max... | {0: [0 - pixel29 (numeric)],
1: [1 - pixel31 (numeric)],
2: [2 - pixel39 (numeric)],
3: [3 - pixel45 (numeric)],
4: [4 - pixel59 (numeric)],
5: [5 - pixel64 (numeric)],
6: [6 - pixel75 (numeric)],
7: [7 - pixel76 (numeric)],
8: [8 - pixel78 (numeric)],
9: [9 - pixel79 (numeric)],
10: [10 - pixel80 (numeric)],... | {'MajorityClassSize': 200.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 200.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | Fashion-MNIST_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"pixel29",
"pixel31",
"pixel39",
"pixel45",
"pixel59",
"pixel64",
"pixel75",
"pixel76",
"pixel78",
"pixel79",
"pixel80",
"pixel107",
"pixel132",
"pixel136",
"pixel142",
"pixel151",
"pixel158",
"pixel163",
"pixel165",
"pixel169",
"pixel180",
"pixel184",
"pixel194",
"pixe... | [
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... | 3,403 |
362,974 | predictive_accuracy | accuracy_score | helena_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset helena (41169) with
seed=1
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int =... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 123.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 3.0,
'NumberOfClasses': 100.0,
'NumberOfFeatures': 28.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 27.0,
'NumberOfSymbolicFeatures': 1.0,
... | helena_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27"
] | [
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
] | 3,405 |
362,976 | predictive_accuracy | accuracy_score | helena_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset helena (41169) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int =... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 123.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 3.0,
'NumberOfClasses': 100.0,
'NumberOfFeatures': 28.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 27.0,
'NumberOfSymbolicFeatures': 1.0,
... | helena_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27"
] | [
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
] | 3,406 |
359,966 | predictive_accuracy | accuracy_score | Internet-Advertisements | **Author**: Nicholas Kushmerick
**Source**: [UCI](http://archive.ics.uci.edu/ml/datasets/Internet+Advertisements) - 1998
**Please cite**:
### Description
__Changes to version 1:__ all categorical features transformed as such.
This dataset represents a set of possible advertisements on Internet pages.
### S... | {0: [0 - height (numeric)],
1: [1 - width (numeric)],
2: [2 - aratio (numeric)],
3: [3 - local (nominal)],
4: [4 - url.images.buttons (nominal)],
5: [5 - url.likesbooks.com (nominal)],
6: [6 - url.www.slake.com (nominal)],
7: [7 - url.hydrogeologist (nominal)],
8: [8 - url.oso (nominal)],
9: [9 - url.media (no... | {'MajorityClassSize': 2820.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 459.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 1559.0,
'NumberOfInstances': 3279.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 155... | Internet-Advertisements | [
"height",
"width",
"aratio",
"local",
"url.images.buttons",
"url.likesbooks.com",
"url.www.slake.com",
"url.hydrogeologist",
"url.oso",
"url.media",
"url.peace.images",
"url.blipverts",
"url.tkaine.kats",
"url.labyrinth",
"url.advertising.blipverts",
"url.images.oso",
"url.area51.cor... | [
false,
false,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
t... | 3,407 |
361,823 | predictive_accuracy | accuracy_score | timing-attack-dataset-8-micro-seconds-delay-2022-09-01 | Bleichenbacher Timing Attack: 8 micro seconds dataset created on 2022-09-01
Attribute Descriptions:
CCS0:tcp.srcport: TCP Source Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.dstport: TCP Destination Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.port: TCP Source or Destination P... | {0: [0 - label (string)],
1: [1 - CCS0:tcp.srcport (numeric)],
2: [2 - CCS0:tcp.dstport (numeric)],
3: [3 - CCS0:tcp.port (numeric)],
4: [4 - CCS0:tcp.stream (numeric)],
5: [5 - CCS0:tcp.len (numeric)],
6: [6 - CCS0:tcp.seq (numeric)],
7: [7 - CCS0:tcp.nxtseq (numeric)],
8: [8 - CCS0:tcp.ack (numeric)],
9: [9 ... | {'MajorityClassSize': 944.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 842.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9995.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-8-micro-seconds-delay-2022-09-01 | [
"CCS0:tcp.srcport",
"CCS0:tcp.dstport",
"CCS0:tcp.port",
"CCS0:tcp.stream",
"CCS0:tcp.len",
"CCS0:tcp.seq",
"CCS0:tcp.nxtseq",
"CCS0:tcp.ack",
"CCS0:tcp.hdr_len",
"CCS0:tcp.flags.res",
"CCS0:tcp.flags.ns",
"CCS0:tcp.flags.cwr",
"CCS0:tcp.flags.ecn",
"CCS0:tcp.flags.urg",
"CCS0:tcp.flags.... | [
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... | 3,408 |
361,840 | predictive_accuracy | accuracy_score | timing-attack-dataset-256-micro-seconds-delay-2022-09-17 | Bleichenbacher Timing Attack: 256 micro seconds dataset created on 2022-09-17
Attribute Descriptions:
CCS0:tcp.srcport: TCP Source Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.dstport: TCP Destination Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.port: TCP Source or Destination... | {0: [0 - label (string)],
1: [1 - CCS0:tcp.srcport (numeric)],
2: [2 - CCS0:tcp.dstport (numeric)],
3: [3 - CCS0:tcp.port (numeric)],
4: [4 - CCS0:tcp.stream (numeric)],
5: [5 - CCS0:tcp.len (numeric)],
6: [6 - CCS0:tcp.seq (numeric)],
7: [7 - CCS0:tcp.nxtseq (numeric)],
8: [8 - CCS0:tcp.ack (numeric)],
9: [9 ... | {'MajorityClassSize': 946.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 855.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9997.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-256-micro-seconds-delay-2022-09-17 | [
"CCS0:tcp.srcport",
"CCS0:tcp.dstport",
"CCS0:tcp.port",
"CCS0:tcp.stream",
"CCS0:tcp.len",
"CCS0:tcp.seq",
"CCS0:tcp.nxtseq",
"CCS0:tcp.ack",
"CCS0:tcp.hdr_len",
"CCS0:tcp.flags.res",
"CCS0:tcp.flags.ns",
"CCS0:tcp.flags.cwr",
"CCS0:tcp.flags.ecn",
"CCS0:tcp.flags.urg",
"CCS0:tcp.flags.... | [
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... | 3,409 |
362,975 | predictive_accuracy | accuracy_score | helena_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset helena (41169) with
seed=2
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int =... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 123.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 3.0,
'NumberOfClasses': 100.0,
'NumberOfFeatures': 28.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 27.0,
'NumberOfSymbolicFeatures': 1.0,
... | helena_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27"
] | [
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
] | 3,410 |
361,815 | predictive_accuracy | accuracy_score | timing-attack-dataset-1-micro-seconds-delay-2022-09-12 | Bleichenbacher Timing Attack: 1 micro seconds dataset created on 2022-09-12
Attribute Descriptions:
CCS0:tcp.srcport: TCP Source Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.dstport: TCP Destination Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.port: TCP Source or Destination P... | {0: [0 - label (string)],
1: [1 - CCS0:tcp.srcport (numeric)],
2: [2 - CCS0:tcp.dstport (numeric)],
3: [3 - CCS0:tcp.port (numeric)],
4: [4 - CCS0:tcp.stream (numeric)],
5: [5 - CCS0:tcp.len (numeric)],
6: [6 - CCS0:tcp.seq (numeric)],
7: [7 - CCS0:tcp.nxtseq (numeric)],
8: [8 - CCS0:tcp.ack (numeric)],
9: [9 ... | {'MajorityClassSize': 948.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 854.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9994.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-1-micro-seconds-delay-2022-09-12 | [
"CCS0:tcp.srcport",
"CCS0:tcp.dstport",
"CCS0:tcp.port",
"CCS0:tcp.stream",
"CCS0:tcp.len",
"CCS0:tcp.seq",
"CCS0:tcp.nxtseq",
"CCS0:tcp.ack",
"CCS0:tcp.hdr_len",
"CCS0:tcp.flags.res",
"CCS0:tcp.flags.ns",
"CCS0:tcp.flags.cwr",
"CCS0:tcp.flags.ecn",
"CCS0:tcp.flags.urg",
"CCS0:tcp.flags.... | [
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... | 3,411 |
363,078 | predictive_accuracy | accuracy_score | dailybike | daily bike dataset | {0: [0 - day (numeric)],
1: [1 - mnth (numeric)],
2: [2 - year (numeric)],
3: [3 - season (numeric)],
4: [4 - holiday (numeric)],
5: [5 - weekday (numeric)],
6: [6 - workingday (numeric)],
7: [7 - weathersit (numeric)],
8: [8 - temp (numeric)],
9: [9 - atemp (numeric)],
10: [10 - hum (numeric)],
11: [11 - wi... | {'MajorityClassSize': 4.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1.0,
'NumberOfClasses': 606.0,
'NumberOfFeatures': 13.0,
'NumberOfInstances': 731.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | dailybike | [
"day",
"mnth",
"year",
"season",
"holiday",
"weekday",
"workingday",
"weathersit",
"temp",
"atemp",
"hum",
"windspeed"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,412 |
363,079 | predictive_accuracy | accuracy_score | dailybike | daily bike dataset | {0: [0 - day (numeric)],
1: [1 - mnth (numeric)],
2: [2 - year (numeric)],
3: [3 - season (numeric)],
4: [4 - holiday (numeric)],
5: [5 - weekday (numeric)],
6: [6 - workingday (numeric)],
7: [7 - weathersit (numeric)],
8: [8 - temp (numeric)],
9: [9 - atemp (numeric)],
10: [10 - hum (numeric)],
11: [11 - wi... | {'MajorityClassSize': 4.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1.0,
'NumberOfClasses': 606.0,
'NumberOfFeatures': 13.0,
'NumberOfInstances': 731.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | dailybike | [
"day",
"mnth",
"year",
"season",
"holiday",
"weekday",
"workingday",
"weathersit",
"temp",
"atemp",
"hum",
"windspeed"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,413 |
363,095 | predictive_accuracy | accuracy_score | Weather | The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some unspecified game. In general, instances in a dataset are characterized by the values of features, or attributes, that me... | {0: [0 - outlook (nominal)],
1: [1 - temperature (numeric)],
2: [2 - humidity (numeric)],
3: [3 - windy (nominal)],
4: [4 - play (nominal)]} | {'MajorityClassSize': 9.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 5.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 14.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 3.0,
'cost_m... | Weather | [
"outlook",
"temperature",
"humidity",
"windy"
] | [
true,
false,
false,
true
] | 3,414 |
361,828 | predictive_accuracy | accuracy_score | timing-attack-dataset-16-micro-seconds-delay-2022-09-17 | Bleichenbacher Timing Attack: 16 micro seconds dataset created on 2022-09-17
Attribute Descriptions:
CCS0:tcp.srcport: TCP Source Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.dstport: TCP Destination Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.port: TCP Source or Destination ... | {0: [0 - label (string)],
1: [1 - CCS0:tcp.srcport (numeric)],
2: [2 - CCS0:tcp.dstport (numeric)],
3: [3 - CCS0:tcp.port (numeric)],
4: [4 - CCS0:tcp.stream (numeric)],
5: [5 - CCS0:tcp.len (numeric)],
6: [6 - CCS0:tcp.seq (numeric)],
7: [7 - CCS0:tcp.nxtseq (numeric)],
8: [8 - CCS0:tcp.ack (numeric)],
9: [9 ... | {'MajorityClassSize': 941.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 873.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9996.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-16-micro-seconds-delay-2022-09-17 | [
"CCS0:tcp.srcport",
"CCS0:tcp.dstport",
"CCS0:tcp.port",
"CCS0:tcp.stream",
"CCS0:tcp.len",
"CCS0:tcp.seq",
"CCS0:tcp.nxtseq",
"CCS0:tcp.ack",
"CCS0:tcp.hdr_len",
"CCS0:tcp.flags.res",
"CCS0:tcp.flags.ns",
"CCS0:tcp.flags.cwr",
"CCS0:tcp.flags.ecn",
"CCS0:tcp.flags.urg",
"CCS0:tcp.flags.... | [
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... | 3,415 |
362,969 | predictive_accuracy | accuracy_score | adult_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset adult (1590) with
seed=1
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int = 1... | {0: [0 - age (numeric)],
1: [1 - workclass (nominal)],
2: [2 - fnlwgt (numeric)],
3: [3 - education (nominal)],
4: [4 - education-num (numeric)],
5: [5 - marital-status (nominal)],
6: [6 - occupation (nominal)],
7: [7 - relationship (nominal)],
8: [8 - race (nominal)],
9: [9 - sex (nominal)],
10: [10 - capita... | {'MajorityClassSize': 1521.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 479.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 15.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 135.0,
'NumberOfMissingValues': 248.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 9... | adult_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"age",
"workclass",
"fnlwgt",
"education",
"education-num",
"marital-status",
"occupation",
"relationship",
"race",
"sex",
"capital-gain",
"capital-loss",
"hours-per-week",
"native-country"
] | [
false,
true,
false,
true,
false,
true,
true,
true,
true,
true,
false,
false,
false,
true
] | 3,416 |
362,898 | predictive_accuracy | accuracy_score | Bioresponse_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Bioresponse (4134) with
seed=0
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: i... | {0: [0 - D5 (numeric)],
1: [1 - D10 (numeric)],
2: [2 - D15 (numeric)],
3: [3 - D28 (numeric)],
4: [4 - D38 (numeric)],
5: [5 - D49 (numeric)],
6: [6 - D58 (numeric)],
7: [7 - D69 (numeric)],
8: [8 - D87 (numeric)],
9: [9 - D127 (numeric)],
10: [10 - D128 (numeric)],
11: [11 - D138 (numeric)],
12: [12 - D15... | {'MajorityClassSize': 1085.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 915.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | Bioresponse_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"D5",
"D10",
"D15",
"D28",
"D38",
"D49",
"D58",
"D69",
"D87",
"D127",
"D128",
"D138",
"D150",
"D153",
"D214",
"D236",
"D296",
"D301",
"D403",
"D444",
"D448",
"D454",
"D467",
"D471",
"D514",
"D518",
"D543",
"D567",
"D580",
"D597",
"D629",
"D656",
"D658"... | [
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... | 3,417 |
361,830 | predictive_accuracy | accuracy_score | timing-attack-dataset-32-micro-seconds-delay-2022-09-12 | Bleichenbacher Timing Attack: 32 micro seconds dataset created on 2022-09-12
Attribute Descriptions:
CCS0:tcp.srcport: TCP Source Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.dstport: TCP Destination Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.port: TCP Source or Destination ... | {0: [0 - label (string)],
1: [1 - CCS0:tcp.srcport (numeric)],
2: [2 - CCS0:tcp.dstport (numeric)],
3: [3 - CCS0:tcp.port (numeric)],
4: [4 - CCS0:tcp.stream (numeric)],
5: [5 - CCS0:tcp.len (numeric)],
6: [6 - CCS0:tcp.seq (numeric)],
7: [7 - CCS0:tcp.nxtseq (numeric)],
8: [8 - CCS0:tcp.ack (numeric)],
9: [9 ... | {'MajorityClassSize': 973.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 837.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9996.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-32-micro-seconds-delay-2022-09-12 | [
"CCS0:tcp.srcport",
"CCS0:tcp.dstport",
"CCS0:tcp.port",
"CCS0:tcp.stream",
"CCS0:tcp.len",
"CCS0:tcp.seq",
"CCS0:tcp.nxtseq",
"CCS0:tcp.ack",
"CCS0:tcp.hdr_len",
"CCS0:tcp.flags.res",
"CCS0:tcp.flags.ns",
"CCS0:tcp.flags.cwr",
"CCS0:tcp.flags.ecn",
"CCS0:tcp.flags.urg",
"CCS0:tcp.flags.... | [
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... | 3,418 |
363,101 | predictive_accuracy | accuracy_score | Weather | The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some unspecified game. In general, instances in a dataset are characterized by the values of features, or attributes, that me... | {0: [0 - outlook (nominal)],
1: [1 - temperature (numeric)],
2: [2 - humidity (numeric)],
3: [3 - windy (nominal)],
4: [4 - play (nominal)]} | {'MajorityClassSize': 9.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 5.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 14.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 3.0,
'cost_m... | Weather | [
"outlook",
"temperature",
"humidity",
"windy"
] | [
true,
false,
false,
true
] | 3,419 |
361,814 | predictive_accuracy | accuracy_score | timing-attack-dataset-1-micro-seconds-delay-2022-09-01 | Bleichenbacher Timing Attack: 1 micro seconds dataset created on 2022-09-01
Attribute Descriptions:
CCS0:tcp.srcport: TCP Source Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.dstport: TCP Destination Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.port: TCP Source or Destination P... | {0: [0 - label (string)],
1: [1 - CCS0:tcp.srcport (numeric)],
2: [2 - CCS0:tcp.dstport (numeric)],
3: [3 - CCS0:tcp.port (numeric)],
4: [4 - CCS0:tcp.stream (numeric)],
5: [5 - CCS0:tcp.len (numeric)],
6: [6 - CCS0:tcp.seq (numeric)],
7: [7 - CCS0:tcp.nxtseq (numeric)],
8: [8 - CCS0:tcp.ack (numeric)],
9: [9 ... | {'MajorityClassSize': 978.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 863.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9995.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-1-micro-seconds-delay-2022-09-01 | [
"CCS0:tcp.srcport",
"CCS0:tcp.dstport",
"CCS0:tcp.port",
"CCS0:tcp.stream",
"CCS0:tcp.len",
"CCS0:tcp.seq",
"CCS0:tcp.nxtseq",
"CCS0:tcp.ack",
"CCS0:tcp.hdr_len",
"CCS0:tcp.flags.res",
"CCS0:tcp.flags.ns",
"CCS0:tcp.flags.cwr",
"CCS0:tcp.flags.ecn",
"CCS0:tcp.flags.urg",
"CCS0:tcp.flags.... | [
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... | 3,420 |
361,817 | predictive_accuracy | accuracy_score | timing-attack-dataset-2-micro-seconds-delay-2022-09-01 | Bleichenbacher Timing Attack: 2 micro seconds dataset created on 2022-09-01
Attribute Descriptions:
CCS0:tcp.srcport: TCP Source Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.dstport: TCP Destination Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.port: TCP Source or Destination P... | {0: [0 - label (string)],
1: [1 - CCS0:tcp.srcport (numeric)],
2: [2 - CCS0:tcp.dstport (numeric)],
3: [3 - CCS0:tcp.port (numeric)],
4: [4 - CCS0:tcp.stream (numeric)],
5: [5 - CCS0:tcp.len (numeric)],
6: [6 - CCS0:tcp.seq (numeric)],
7: [7 - CCS0:tcp.nxtseq (numeric)],
8: [8 - CCS0:tcp.ack (numeric)],
9: [9 ... | {'MajorityClassSize': 971.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 855.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9995.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-2-micro-seconds-delay-2022-09-01 | [
"CCS0:tcp.srcport",
"CCS0:tcp.dstport",
"CCS0:tcp.port",
"CCS0:tcp.stream",
"CCS0:tcp.len",
"CCS0:tcp.seq",
"CCS0:tcp.nxtseq",
"CCS0:tcp.ack",
"CCS0:tcp.hdr_len",
"CCS0:tcp.flags.res",
"CCS0:tcp.flags.ns",
"CCS0:tcp.flags.cwr",
"CCS0:tcp.flags.ecn",
"CCS0:tcp.flags.urg",
"CCS0:tcp.flags.... | [
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... | 3,421 |
361,816 | predictive_accuracy | accuracy_score | timing-attack-dataset-1-micro-seconds-delay-2022-09-17 | Bleichenbacher Timing Attack: 1 micro seconds dataset created on 2022-09-17
Attribute Descriptions:
CCS0:tcp.srcport: TCP Source Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.dstport: TCP Destination Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.port: TCP Source or Destination P... | {0: [0 - label (string)],
1: [1 - CCS0:tcp.srcport (numeric)],
2: [2 - CCS0:tcp.dstport (numeric)],
3: [3 - CCS0:tcp.port (numeric)],
4: [4 - CCS0:tcp.stream (numeric)],
5: [5 - CCS0:tcp.len (numeric)],
6: [6 - CCS0:tcp.seq (numeric)],
7: [7 - CCS0:tcp.nxtseq (numeric)],
8: [8 - CCS0:tcp.ack (numeric)],
9: [9 ... | {'MajorityClassSize': 970.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 850.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9994.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-1-micro-seconds-delay-2022-09-17 | [
"CCS0:tcp.srcport",
"CCS0:tcp.dstport",
"CCS0:tcp.port",
"CCS0:tcp.stream",
"CCS0:tcp.len",
"CCS0:tcp.seq",
"CCS0:tcp.nxtseq",
"CCS0:tcp.ack",
"CCS0:tcp.hdr_len",
"CCS0:tcp.flags.res",
"CCS0:tcp.flags.ns",
"CCS0:tcp.flags.cwr",
"CCS0:tcp.flags.ecn",
"CCS0:tcp.flags.urg",
"CCS0:tcp.flags.... | [
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... | 3,422 |
363,098 | predictive_accuracy | accuracy_score | Weather | The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some unspecified game. In general, instances in a dataset are characterized by the values of features, or attributes, that me... | {0: [0 - outlook (nominal)],
1: [1 - temperature (numeric)],
2: [2 - humidity (numeric)],
3: [3 - windy (nominal)],
4: [4 - play (nominal)]} | {'MajorityClassSize': 9.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 5.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 14.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 3.0,
'cost_m... | Weather | [
"outlook",
"temperature",
"humidity",
"windy"
] | [
true,
false,
false,
true
] | 3,423 |
361,824 | predictive_accuracy | accuracy_score | timing-attack-dataset-8-micro-seconds-delay-2022-09-12 | Bleichenbacher Timing Attack: 8 micro seconds dataset created on 2022-09-12
Attribute Descriptions:
CCS0:tcp.srcport: TCP Source Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.dstport: TCP Destination Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.port: TCP Source or Destination P... | {0: [0 - label (string)],
1: [1 - CCS0:tcp.srcport (numeric)],
2: [2 - CCS0:tcp.dstport (numeric)],
3: [3 - CCS0:tcp.port (numeric)],
4: [4 - CCS0:tcp.stream (numeric)],
5: [5 - CCS0:tcp.len (numeric)],
6: [6 - CCS0:tcp.seq (numeric)],
7: [7 - CCS0:tcp.nxtseq (numeric)],
8: [8 - CCS0:tcp.ack (numeric)],
9: [9 ... | {'MajorityClassSize': 966.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 849.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9996.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-8-micro-seconds-delay-2022-09-12 | [
"CCS0:tcp.srcport",
"CCS0:tcp.dstport",
"CCS0:tcp.port",
"CCS0:tcp.stream",
"CCS0:tcp.len",
"CCS0:tcp.seq",
"CCS0:tcp.nxtseq",
"CCS0:tcp.ack",
"CCS0:tcp.hdr_len",
"CCS0:tcp.flags.res",
"CCS0:tcp.flags.ns",
"CCS0:tcp.flags.cwr",
"CCS0:tcp.flags.ecn",
"CCS0:tcp.flags.urg",
"CCS0:tcp.flags.... | [
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... | 3,424 |
363,026 | predictive_accuracy | accuracy_score | KDDCup99_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset KDDCup99 (42746) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int... | {0: [0 - duration (numeric)],
1: [1 - protocol_type (nominal)],
2: [2 - service (nominal)],
3: [3 - flag (nominal)],
4: [4 - src_bytes (numeric)],
5: [5 - dst_bytes (numeric)],
6: [6 - land (nominal)],
7: [7 - wrong_fragment (numeric)],
8: [8 - urgent (numeric)],
9: [9 - hot (numeric)],
10: [10 - num_failed_l... | {'MajorityClassSize': 1147.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1.0,
'NumberOfClasses': 8.0,
'NumberOfFeatures': 42.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 32.0,
'NumberOfSymbolicFeatures': 10.0,
... | KDDCup99_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"duration",
"protocol_type",
"service",
"flag",
"src_bytes",
"dst_bytes",
"land",
"wrong_fragment",
"urgent",
"hot",
"num_failed_logins",
"logged_in",
"num_compromised",
"root_shell",
"su_attempted",
"num_root",
"num_file_creations",
"num_shells",
"num_access_files",
"num_outbo... | [
false,
true,
true,
true,
false,
false,
true,
false,
false,
false,
false,
true,
false,
true,
true,
false,
false,
false,
false,
false,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 3,425 |
361,833 | predictive_accuracy | accuracy_score | timing-attack-dataset-64-micro-seconds-delay-2022-09-12 | Bleichenbacher Timing Attack: 64 micro seconds dataset created on 2022-09-12
Attribute Descriptions:
CCS0:tcp.srcport: TCP Source Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.dstport: TCP Destination Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.port: TCP Source or Destination ... | {0: [0 - label (string)],
1: [1 - CCS0:tcp.srcport (numeric)],
2: [2 - CCS0:tcp.dstport (numeric)],
3: [3 - CCS0:tcp.port (numeric)],
4: [4 - CCS0:tcp.stream (numeric)],
5: [5 - CCS0:tcp.len (numeric)],
6: [6 - CCS0:tcp.seq (numeric)],
7: [7 - CCS0:tcp.nxtseq (numeric)],
8: [8 - CCS0:tcp.ack (numeric)],
9: [9 ... | {'MajorityClassSize': 968.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 832.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9997.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-64-micro-seconds-delay-2022-09-12 | [
"CCS0:tcp.srcport",
"CCS0:tcp.dstport",
"CCS0:tcp.port",
"CCS0:tcp.stream",
"CCS0:tcp.len",
"CCS0:tcp.seq",
"CCS0:tcp.nxtseq",
"CCS0:tcp.ack",
"CCS0:tcp.hdr_len",
"CCS0:tcp.flags.res",
"CCS0:tcp.flags.ns",
"CCS0:tcp.flags.cwr",
"CCS0:tcp.flags.ecn",
"CCS0:tcp.flags.urg",
"CCS0:tcp.flags.... | [
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... | 3,426 |
363,022 | predictive_accuracy | accuracy_score | KDDCup99_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset KDDCup99 (42746) with
seed=2
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int... | {0: [0 - duration (numeric)],
1: [1 - protocol_type (nominal)],
2: [2 - service (nominal)],
3: [3 - flag (nominal)],
4: [4 - src_bytes (numeric)],
5: [5 - dst_bytes (numeric)],
6: [6 - land (nominal)],
7: [7 - wrong_fragment (numeric)],
8: [8 - urgent (numeric)],
9: [9 - hot (numeric)],
10: [10 - num_failed_l... | {'MajorityClassSize': 1147.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1.0,
'NumberOfClasses': 8.0,
'NumberOfFeatures': 42.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 32.0,
'NumberOfSymbolicFeatures': 10.0,
... | KDDCup99_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"duration",
"protocol_type",
"service",
"flag",
"src_bytes",
"dst_bytes",
"land",
"wrong_fragment",
"urgent",
"hot",
"num_failed_logins",
"logged_in",
"num_compromised",
"root_shell",
"su_attempted",
"num_root",
"num_file_creations",
"num_shells",
"num_access_files",
"num_outbo... | [
false,
true,
true,
true,
false,
false,
true,
false,
false,
false,
false,
true,
false,
true,
true,
false,
false,
false,
false,
false,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 3,427 |
363,023 | predictive_accuracy | accuracy_score | KDDCup99_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset KDDCup99 (42746) with
seed=1
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int... | {0: [0 - duration (numeric)],
1: [1 - protocol_type (nominal)],
2: [2 - service (nominal)],
3: [3 - flag (nominal)],
4: [4 - src_bytes (numeric)],
5: [5 - dst_bytes (numeric)],
6: [6 - land (nominal)],
7: [7 - wrong_fragment (numeric)],
8: [8 - urgent (numeric)],
9: [9 - hot (numeric)],
10: [10 - num_failed_l... | {'MajorityClassSize': 1147.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1.0,
'NumberOfClasses': 8.0,
'NumberOfFeatures': 42.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 32.0,
'NumberOfSymbolicFeatures': 10.0,
... | KDDCup99_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"duration",
"protocol_type",
"service",
"flag",
"src_bytes",
"dst_bytes",
"land",
"wrong_fragment",
"urgent",
"hot",
"num_failed_logins",
"logged_in",
"num_compromised",
"root_shell",
"su_attempted",
"num_root",
"num_file_creations",
"num_shells",
"num_access_files",
"num_outbo... | [
false,
true,
true,
true,
false,
false,
true,
false,
false,
false,
false,
true,
false,
true,
true,
false,
false,
false,
false,
false,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 3,428 |
362,928 | predictive_accuracy | accuracy_score | christine_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset christine (41142) with
seed=0
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: in... | {0: [0 - V5 (numeric)],
1: [1 - V9 (numeric)],
2: [2 - V14 (numeric)],
3: [3 - V26 (numeric)],
4: [4 - V35 (numeric)],
5: [5 - V45 (numeric)],
6: [6 - V53 (numeric)],
7: [7 - V64 (numeric)],
8: [8 - V80 (numeric)],
9: [9 - V117 (numeric)],
10: [10 - V118 (numeric)],
11: [11 - V127 (numeric)],
12: [12 - V138... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 96.0,
'NumberOfSymbolicFeatures': 5.... | christine_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V5",
"V9",
"V14",
"V26",
"V35",
"V45",
"V53",
"V64",
"V80",
"V117",
"V118",
"V127",
"V138",
"V141",
"V197",
"V217",
"V271",
"V276",
"V371",
"V408",
"V413",
"V416",
"V430",
"V432",
"V472",
"V475",
"V499",
"V522",
"V534",
"V549",
"V579",
"V604",
"V605",... | [
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... | 3,429 |
363,105 | predictive_accuracy | accuracy_score | Weather | The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some unspecified game. In general, instances in a dataset are characterized by the values of features, or attributes, that me... | {0: [0 - outlook (nominal)],
1: [1 - temperature (numeric)],
2: [2 - humidity (numeric)],
3: [3 - windy (nominal)],
4: [4 - play (nominal)]} | {'MajorityClassSize': 9.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 5.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 14.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 3.0,
'cost_m... | Weather | [
"outlook",
"temperature",
"humidity",
"windy"
] | [
true,
false,
false,
true
] | 3,430 |
361,897 | predictive_accuracy | accuracy_score | timing-attack-dataset-35-micro-seconds-delay-2022-09-18 | Bleichenbacher Timing Attack: 35 micro seconds dataset created on 2022-09-18
Attribute Descriptions:
CCS0:tcp.srcport: TCP Source Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.dstport: TCP Destination Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.port: TCP Source or Destination ... | {0: [0 - label (string)],
1: [1 - CCS0:tcp.srcport (numeric)],
2: [2 - CCS0:tcp.dstport (numeric)],
3: [3 - CCS0:tcp.port (numeric)],
4: [4 - CCS0:tcp.stream (numeric)],
5: [5 - CCS0:tcp.len (numeric)],
6: [6 - CCS0:tcp.seq (numeric)],
7: [7 - CCS0:tcp.nxtseq (numeric)],
8: [8 - CCS0:tcp.ack (numeric)],
9: [9 ... | {'MajorityClassSize': 938.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 873.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9997.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-35-micro-seconds-delay-2022-09-18 | [
"CCS0:tcp.srcport",
"CCS0:tcp.dstport",
"CCS0:tcp.port",
"CCS0:tcp.stream",
"CCS0:tcp.len",
"CCS0:tcp.seq",
"CCS0:tcp.nxtseq",
"CCS0:tcp.ack",
"CCS0:tcp.hdr_len",
"CCS0:tcp.flags.res",
"CCS0:tcp.flags.ns",
"CCS0:tcp.flags.cwr",
"CCS0:tcp.flags.ecn",
"CCS0:tcp.flags.urg",
"CCS0:tcp.flags.... | [
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... | 3,431 |
363,102 | predictive_accuracy | accuracy_score | Weather | The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some unspecified game. In general, instances in a dataset are characterized by the values of features, or attributes, that me... | {0: [0 - outlook (nominal)],
1: [1 - temperature (numeric)],
2: [2 - humidity (numeric)],
3: [3 - windy (nominal)],
4: [4 - play (nominal)]} | {'MajorityClassSize': 9.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 5.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 14.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 3.0,
'cost_m... | Weather | [
"outlook",
"temperature",
"humidity",
"windy"
] | [
true,
false,
false,
true
] | 3,432 |
363,004 | predictive_accuracy | accuracy_score | kick_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset kick (41162) with
seed=2
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int = 1... | {0: [0 - PurchDate (numeric)],
1: [1 - Auction (nominal)],
2: [2 - VehYear (numeric)],
3: [3 - VehicleAge (numeric)],
4: [4 - Make (nominal)],
5: [5 - Model (nominal)],
6: [6 - Trim (nominal)],
7: [7 - SubModel (nominal)],
8: [8 - Color (nominal)],
9: [9 - Transmission (nominal)],
10: [10 - WheelTypeID (nomin... | {'MajorityClassSize': 1754.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 246.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 33.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 1892.0,
'NumberOfMissingValues': 4045.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures'... | kick_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"PurchDate",
"Auction",
"VehYear",
"VehicleAge",
"Make",
"Model",
"Trim",
"SubModel",
"Color",
"Transmission",
"WheelTypeID",
"WheelType",
"VehOdo",
"Nationality",
"Size",
"TopThreeAmericanName",
"MMRAcquisitionAuctionAveragePrice",
"MMRAcquisitionAuctionCleanPrice",
"MMRAcquisit... | [
false,
true,
false,
false,
true,
true,
true,
true,
true,
true,
true,
true,
false,
true,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
true,
true,
true,
true,
true,
false,
true,
false
] | 3,434 |
362,930 | predictive_accuracy | accuracy_score | christine_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset christine (41142) with
seed=2
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: in... | {0: [0 - V63 (numeric)],
1: [1 - V64 (numeric)],
2: [2 - V86 (numeric)],
3: [3 - V93 (numeric)],
4: [4 - V125 (numeric)],
5: [5 - V142 (numeric)],
6: [6 - V158 (numeric)],
7: [7 - V165 (numeric)],
8: [8 - V167 (numeric)],
9: [9 - V169 (numeric)],
10: [10 - V171 (numeric)],
11: [11 - V235 (numeric)],
12: [12... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 97.0,
'NumberOfSymbolicFeatures': 4.... | christine_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V63",
"V64",
"V86",
"V93",
"V125",
"V142",
"V158",
"V165",
"V167",
"V169",
"V171",
"V235",
"V292",
"V296",
"V314",
"V323",
"V345",
"V353",
"V356",
"V403",
"V406",
"V423",
"V428",
"V460",
"V476",
"V505",
"V518",
"V526",
"V546",
"V552",
"V599",
"V617",
... | [
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,
true,
false,
false,
false,
false,
false,
false,
fa... | 3,435 |
363,007 | predictive_accuracy | accuracy_score | kick_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset kick (41162) with
seed=4
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int = 1... | {0: [0 - PurchDate (numeric)],
1: [1 - Auction (nominal)],
2: [2 - VehYear (numeric)],
3: [3 - VehicleAge (numeric)],
4: [4 - Make (nominal)],
5: [5 - Model (nominal)],
6: [6 - Trim (nominal)],
7: [7 - SubModel (nominal)],
8: [8 - Color (nominal)],
9: [9 - Transmission (nominal)],
10: [10 - WheelTypeID (nomin... | {'MajorityClassSize': 1754.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 246.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 33.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 1915.0,
'NumberOfMissingValues': 4082.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures'... | kick_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"PurchDate",
"Auction",
"VehYear",
"VehicleAge",
"Make",
"Model",
"Trim",
"SubModel",
"Color",
"Transmission",
"WheelTypeID",
"WheelType",
"VehOdo",
"Nationality",
"Size",
"TopThreeAmericanName",
"MMRAcquisitionAuctionAveragePrice",
"MMRAcquisitionAuctionCleanPrice",
"MMRAcquisit... | [
false,
true,
false,
false,
true,
true,
true,
true,
true,
true,
true,
true,
false,
true,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
true,
true,
true,
true,
true,
false,
true,
false
] | 3,436 |
361,861 | predictive_accuracy | accuracy_score | timing-attack-dataset-20-micro-seconds-delay-2022-09-04 | Bleichenbacher Timing Attack: 20 micro seconds dataset created on 2022-09-04
Attribute Descriptions:
CCS0:tcp.srcport: TCP Source Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.dstport: TCP Destination Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.port: TCP Source or Destination ... | {0: [0 - label (string)],
1: [1 - CCS0:tcp.srcport (numeric)],
2: [2 - CCS0:tcp.dstport (numeric)],
3: [3 - CCS0:tcp.port (numeric)],
4: [4 - CCS0:tcp.stream (numeric)],
5: [5 - CCS0:tcp.len (numeric)],
6: [6 - CCS0:tcp.seq (numeric)],
7: [7 - CCS0:tcp.nxtseq (numeric)],
8: [8 - CCS0:tcp.ack (numeric)],
9: [9 ... | {'MajorityClassSize': 947.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 889.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9997.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-20-micro-seconds-delay-2022-09-04 | [
"CCS0:tcp.srcport",
"CCS0:tcp.dstport",
"CCS0:tcp.port",
"CCS0:tcp.stream",
"CCS0:tcp.len",
"CCS0:tcp.seq",
"CCS0:tcp.nxtseq",
"CCS0:tcp.ack",
"CCS0:tcp.hdr_len",
"CCS0:tcp.flags.res",
"CCS0:tcp.flags.ns",
"CCS0:tcp.flags.cwr",
"CCS0:tcp.flags.ecn",
"CCS0:tcp.flags.urg",
"CCS0:tcp.flags.... | [
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... | 3,437 |
363,021 | predictive_accuracy | accuracy_score | KDDCup99_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset KDDCup99 (42746) with
seed=0
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int... | {0: [0 - duration (numeric)],
1: [1 - protocol_type (nominal)],
2: [2 - service (nominal)],
3: [3 - flag (nominal)],
4: [4 - src_bytes (numeric)],
5: [5 - dst_bytes (numeric)],
6: [6 - land (nominal)],
7: [7 - wrong_fragment (numeric)],
8: [8 - urgent (numeric)],
9: [9 - hot (numeric)],
10: [10 - num_failed_l... | {'MajorityClassSize': 1147.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1.0,
'NumberOfClasses': 8.0,
'NumberOfFeatures': 42.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 32.0,
'NumberOfSymbolicFeatures': 10.0,
... | KDDCup99_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"duration",
"protocol_type",
"service",
"flag",
"src_bytes",
"dst_bytes",
"land",
"wrong_fragment",
"urgent",
"hot",
"num_failed_logins",
"logged_in",
"num_compromised",
"root_shell",
"su_attempted",
"num_root",
"num_file_creations",
"num_shells",
"num_access_files",
"num_outbo... | [
false,
true,
true,
true,
false,
false,
true,
false,
false,
false,
false,
true,
false,
true,
true,
false,
false,
false,
false,
false,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 3,438 |
361,821 | predictive_accuracy | accuracy_score | timing-attack-dataset-4-micro-seconds-delay-2022-09-12 | Bleichenbacher Timing Attack: 4 micro seconds dataset created on 2022-09-12
Attribute Descriptions:
CCS0:tcp.srcport: TCP Source Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.dstport: TCP Destination Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.port: TCP Source or Destination P... | {0: [0 - label (string)],
1: [1 - CCS0:tcp.srcport (numeric)],
2: [2 - CCS0:tcp.dstport (numeric)],
3: [3 - CCS0:tcp.port (numeric)],
4: [4 - CCS0:tcp.stream (numeric)],
5: [5 - CCS0:tcp.len (numeric)],
6: [6 - CCS0:tcp.seq (numeric)],
7: [7 - CCS0:tcp.nxtseq (numeric)],
8: [8 - CCS0:tcp.ack (numeric)],
9: [9 ... | {'MajorityClassSize': 967.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 850.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9996.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-4-micro-seconds-delay-2022-09-12 | [
"CCS0:tcp.srcport",
"CCS0:tcp.dstport",
"CCS0:tcp.port",
"CCS0:tcp.stream",
"CCS0:tcp.len",
"CCS0:tcp.seq",
"CCS0:tcp.nxtseq",
"CCS0:tcp.ack",
"CCS0:tcp.hdr_len",
"CCS0:tcp.flags.res",
"CCS0:tcp.flags.ns",
"CCS0:tcp.flags.cwr",
"CCS0:tcp.flags.ecn",
"CCS0:tcp.flags.urg",
"CCS0:tcp.flags.... | [
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... | 3,439 |
168,791 | predictive_accuracy | accuracy_score | Internet-Advertisements | **Author**: Nicholas Kushmerick
**Source**: [UCI](http://archive.ics.uci.edu/ml/datasets/Internet+Advertisements) - 1998
**Please cite**:
### Description
__Changes to version 1:__ all categorical features transformed as such.
This dataset represents a set of possible advertisements on Internet pages.
### S... | {0: [0 - height (numeric)],
1: [1 - width (numeric)],
2: [2 - aratio (numeric)],
3: [3 - local (nominal)],
4: [4 - url.images.buttons (nominal)],
5: [5 - url.likesbooks.com (nominal)],
6: [6 - url.www.slake.com (nominal)],
7: [7 - url.hydrogeologist (nominal)],
8: [8 - url.oso (nominal)],
9: [9 - url.media (no... | {'MajorityClassSize': 2820.0,
'MaxNominalAttDistinctValues': 2.0,
'MinorityClassSize': 459.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 1559.0,
'NumberOfInstances': 3279.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 155... | Internet-Advertisements | [
"height",
"width",
"aratio",
"local",
"url.images.buttons",
"url.likesbooks.com",
"url.www.slake.com",
"url.hydrogeologist",
"url.oso",
"url.media",
"url.peace.images",
"url.blipverts",
"url.tkaine.kats",
"url.labyrinth",
"url.advertising.blipverts",
"url.images.oso",
"url.area51.cor... | [
false,
false,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
t... | 3,440 |
363,027 | predictive_accuracy | accuracy_score | KDDCup99_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset KDDCup99 (42746) with
seed=4
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int... | {0: [0 - duration (numeric)],
1: [1 - protocol_type (nominal)],
2: [2 - service (nominal)],
3: [3 - flag (nominal)],
4: [4 - src_bytes (numeric)],
5: [5 - dst_bytes (numeric)],
6: [6 - land (nominal)],
7: [7 - wrong_fragment (numeric)],
8: [8 - urgent (numeric)],
9: [9 - hot (numeric)],
10: [10 - num_failed_l... | {'MajorityClassSize': 1147.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1.0,
'NumberOfClasses': 8.0,
'NumberOfFeatures': 42.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 32.0,
'NumberOfSymbolicFeatures': 10.0,
... | KDDCup99_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"duration",
"protocol_type",
"service",
"flag",
"src_bytes",
"dst_bytes",
"land",
"wrong_fragment",
"urgent",
"hot",
"num_failed_logins",
"logged_in",
"num_compromised",
"root_shell",
"su_attempted",
"num_root",
"num_file_creations",
"num_shells",
"num_access_files",
"num_outbo... | [
false,
true,
true,
true,
false,
false,
true,
false,
false,
false,
false,
true,
false,
true,
true,
false,
false,
false,
false,
false,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 3,441 |
363,107 | predictive_accuracy | accuracy_score | Weather | The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some unspecified game. In general, instances in a dataset are characterized by the values of features, or attributes, that me... | {0: [0 - outlook (nominal)],
1: [1 - temperature (numeric)],
2: [2 - humidity (numeric)],
3: [3 - windy (nominal)],
4: [4 - play (nominal)]} | {'MajorityClassSize': 9.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 5.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 14.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 3.0,
'cost_m... | Weather | [
"outlook",
"temperature",
"humidity",
"windy"
] | [
true,
false,
false,
true
] | 3,442 |
361,822 | predictive_accuracy | accuracy_score | timing-attack-dataset-4-micro-seconds-delay-2022-09-17 | Bleichenbacher Timing Attack: 4 micro seconds dataset created on 2022-09-17
Attribute Descriptions:
CCS0:tcp.srcport: TCP Source Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.dstport: TCP Destination Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.port: TCP Source or Destination P... | {0: [0 - label (string)],
1: [1 - CCS0:tcp.srcport (numeric)],
2: [2 - CCS0:tcp.dstport (numeric)],
3: [3 - CCS0:tcp.port (numeric)],
4: [4 - CCS0:tcp.stream (numeric)],
5: [5 - CCS0:tcp.len (numeric)],
6: [6 - CCS0:tcp.seq (numeric)],
7: [7 - CCS0:tcp.nxtseq (numeric)],
8: [8 - CCS0:tcp.ack (numeric)],
9: [9 ... | {'MajorityClassSize': 984.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 829.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9993.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-4-micro-seconds-delay-2022-09-17 | [
"CCS0:tcp.srcport",
"CCS0:tcp.dstport",
"CCS0:tcp.port",
"CCS0:tcp.stream",
"CCS0:tcp.len",
"CCS0:tcp.seq",
"CCS0:tcp.nxtseq",
"CCS0:tcp.ack",
"CCS0:tcp.hdr_len",
"CCS0:tcp.flags.res",
"CCS0:tcp.flags.ns",
"CCS0:tcp.flags.cwr",
"CCS0:tcp.flags.ecn",
"CCS0:tcp.flags.urg",
"CCS0:tcp.flags.... | [
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... | 3,443 |
363,103 | predictive_accuracy | accuracy_score | Weather | The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some unspecified game. In general, instances in a dataset are characterized by the values of features, or attributes, that me... | {0: [0 - outlook (nominal)],
1: [1 - temperature (numeric)],
2: [2 - humidity (numeric)],
3: [3 - windy (nominal)],
4: [4 - play (nominal)]} | {'MajorityClassSize': 9.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 5.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 14.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 3.0,
'cost_m... | Weather | [
"outlook",
"temperature",
"humidity",
"windy"
] | [
true,
false,
false,
true
] | 3,444 |
363,110 | predictive_accuracy | accuracy_score | Weather-Test | The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some unspecified game. In general, instances in a dataset are characterized by the values of features, or attributes, that me... | {0: [0 - outlook (nominal)],
1: [1 - temperature (numeric)],
2: [2 - humidity (numeric)],
3: [3 - windy (nominal)],
4: [4 - play (nominal)]} | {'MajorityClassSize': 9.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 5.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 14.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 3.0,
'cost_m... | Weather-Test | [
"outlook",
"temperature",
"humidity",
"windy"
] | [
true,
false,
false,
true
] | 3,445 |
362,931 | predictive_accuracy | accuracy_score | christine_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset christine (41142) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: in... | {0: [0 - V3 (numeric)],
1: [1 - V8 (numeric)],
2: [2 - V49 (numeric)],
3: [3 - V51 (numeric)],
4: [4 - V61 (numeric)],
5: [5 - V69 (numeric)],
6: [6 - V122 (numeric)],
7: [7 - V132 (numeric)],
8: [8 - V146 (numeric)],
9: [9 - V167 (numeric)],
10: [10 - V177 (numeric)],
11: [11 - V221 (numeric)],
12: [12 - V... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1... | christine_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V3",
"V8",
"V49",
"V51",
"V61",
"V69",
"V122",
"V132",
"V146",
"V167",
"V177",
"V221",
"V248",
"V271",
"V277",
"V280",
"V303",
"V333",
"V354",
"V365",
"V366",
"V389",
"V399",
"V406",
"V411",
"V447",
"V462",
"V472",
"V473",
"V481",
"V498",
"V503",
"V51... | [
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... | 3,446 |
363,006 | predictive_accuracy | accuracy_score | kick_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset kick (41162) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int = 1... | {0: [0 - PurchDate (numeric)],
1: [1 - Auction (nominal)],
2: [2 - VehYear (numeric)],
3: [3 - VehicleAge (numeric)],
4: [4 - Make (nominal)],
5: [5 - Model (nominal)],
6: [6 - Trim (nominal)],
7: [7 - SubModel (nominal)],
8: [8 - Color (nominal)],
9: [9 - Transmission (nominal)],
10: [10 - WheelTypeID (nomin... | {'MajorityClassSize': 1754.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 246.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 33.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 1922.0,
'NumberOfMissingValues': 4127.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures'... | kick_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"PurchDate",
"Auction",
"VehYear",
"VehicleAge",
"Make",
"Model",
"Trim",
"SubModel",
"Color",
"Transmission",
"WheelTypeID",
"WheelType",
"VehOdo",
"Nationality",
"Size",
"TopThreeAmericanName",
"MMRAcquisitionAuctionAveragePrice",
"MMRAcquisitionAuctionCleanPrice",
"MMRAcquisit... | [
false,
true,
false,
false,
true,
true,
true,
true,
true,
true,
true,
true,
false,
true,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
true,
true,
true,
true,
true,
false,
true,
false
] | 3,447 |
361,826 | predictive_accuracy | accuracy_score | timing-attack-dataset-16-micro-seconds-delay-2022-09-01 | Bleichenbacher Timing Attack: 16 micro seconds dataset created on 2022-09-01
Attribute Descriptions:
CCS0:tcp.srcport: TCP Source Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.dstport: TCP Destination Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.port: TCP Source or Destination ... | {0: [0 - label (string)],
1: [1 - CCS0:tcp.srcport (numeric)],
2: [2 - CCS0:tcp.dstport (numeric)],
3: [3 - CCS0:tcp.port (numeric)],
4: [4 - CCS0:tcp.stream (numeric)],
5: [5 - CCS0:tcp.len (numeric)],
6: [6 - CCS0:tcp.seq (numeric)],
7: [7 - CCS0:tcp.nxtseq (numeric)],
8: [8 - CCS0:tcp.ack (numeric)],
9: [9 ... | {'MajorityClassSize': 953.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 869.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9995.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-16-micro-seconds-delay-2022-09-01 | [
"CCS0:tcp.srcport",
"CCS0:tcp.dstport",
"CCS0:tcp.port",
"CCS0:tcp.stream",
"CCS0:tcp.len",
"CCS0:tcp.seq",
"CCS0:tcp.nxtseq",
"CCS0:tcp.ack",
"CCS0:tcp.hdr_len",
"CCS0:tcp.flags.res",
"CCS0:tcp.flags.ns",
"CCS0:tcp.flags.cwr",
"CCS0:tcp.flags.ecn",
"CCS0:tcp.flags.urg",
"CCS0:tcp.flags.... | [
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... | 3,448 |
363,003 | predictive_accuracy | accuracy_score | kick_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset kick (41162) with
seed=1
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int = 1... | {0: [0 - PurchDate (numeric)],
1: [1 - Auction (nominal)],
2: [2 - VehYear (numeric)],
3: [3 - VehicleAge (numeric)],
4: [4 - Make (nominal)],
5: [5 - Model (nominal)],
6: [6 - Trim (nominal)],
7: [7 - SubModel (nominal)],
8: [8 - Color (nominal)],
9: [9 - Transmission (nominal)],
10: [10 - WheelTypeID (nomin... | {'MajorityClassSize': 1754.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 246.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 33.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 1914.0,
'NumberOfMissingValues': 4109.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures'... | kick_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"PurchDate",
"Auction",
"VehYear",
"VehicleAge",
"Make",
"Model",
"Trim",
"SubModel",
"Color",
"Transmission",
"WheelTypeID",
"WheelType",
"VehOdo",
"Nationality",
"Size",
"TopThreeAmericanName",
"MMRAcquisitionAuctionAveragePrice",
"MMRAcquisitionAuctionCleanPrice",
"MMRAcquisit... | [
false,
true,
false,
false,
true,
true,
true,
true,
true,
true,
true,
true,
false,
true,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
true,
true,
true,
true,
true,
false,
true,
false
] | 3,449 |
361,818 | predictive_accuracy | accuracy_score | timing-attack-dataset-2-micro-seconds-delay-2022-09-12 | Bleichenbacher Timing Attack: 2 micro seconds dataset created on 2022-09-12
Attribute Descriptions:
CCS0:tcp.srcport: TCP Source Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.dstport: TCP Destination Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.port: TCP Source or Destination P... | {0: [0 - label (string)],
1: [1 - CCS0:tcp.srcport (numeric)],
2: [2 - CCS0:tcp.dstport (numeric)],
3: [3 - CCS0:tcp.port (numeric)],
4: [4 - CCS0:tcp.stream (numeric)],
5: [5 - CCS0:tcp.len (numeric)],
6: [6 - CCS0:tcp.seq (numeric)],
7: [7 - CCS0:tcp.nxtseq (numeric)],
8: [8 - CCS0:tcp.ack (numeric)],
9: [9 ... | {'MajorityClassSize': 941.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 855.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9990.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-2-micro-seconds-delay-2022-09-12 | [
"CCS0:tcp.srcport",
"CCS0:tcp.dstport",
"CCS0:tcp.port",
"CCS0:tcp.stream",
"CCS0:tcp.len",
"CCS0:tcp.seq",
"CCS0:tcp.nxtseq",
"CCS0:tcp.ack",
"CCS0:tcp.hdr_len",
"CCS0:tcp.flags.res",
"CCS0:tcp.flags.ns",
"CCS0:tcp.flags.cwr",
"CCS0:tcp.flags.ecn",
"CCS0:tcp.flags.urg",
"CCS0:tcp.flags.... | [
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... | 3,450 |
363,111 | predictive_accuracy | accuracy_score | Weather | The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some unspecified game. In general, instances in a dataset are characterized by the values of features, or attributes, that me... | {0: [0 - outlook (nominal)],
1: [1 - temperature (numeric)],
2: [2 - humidity (numeric)],
3: [3 - windy (nominal)],
4: [4 - play (nominal)]} | {'MajorityClassSize': 9.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 5.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 14.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 3.0,
'cost_m... | Weather | [
"outlook",
"temperature",
"humidity",
"windy"
] | [
true,
false,
false,
true
] | 3,451 |
363,116 | predictive_accuracy | accuracy_score | Weather | The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some unspecified game. In general, instances in a dataset are characterized by the values of features, or attributes, that me... | {0: [0 - outlook (nominal)],
1: [1 - temperature (numeric)],
2: [2 - humidity (numeric)],
3: [3 - windy (nominal)],
4: [4 - play (nominal)]} | {'MajorityClassSize': 9.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 5.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 14.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 3.0,
'cost_m... | Weather | [
"outlook",
"temperature",
"humidity",
"windy"
] | [
true,
false,
false,
true
] | 3,452 |
363,109 | predictive_accuracy | accuracy_score | Weather | The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some unspecified game. In general, instances in a dataset are characterized by the values of features, or attributes, that me... | {0: [0 - outlook (nominal)],
1: [1 - temperature (numeric)],
2: [2 - humidity (numeric)],
3: [3 - windy (nominal)],
4: [4 - play (nominal)]} | {'MajorityClassSize': 9.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 5.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 14.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 3.0,
'cost_m... | Weather | [
"outlook",
"temperature",
"humidity",
"windy"
] | [
true,
false,
false,
true
] | 3,453 |
363,115 | predictive_accuracy | accuracy_score | Weather | The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some unspecified game. In general, instances in a dataset are characterized by the values of features, or attributes, that me... | {0: [0 - outlook (nominal)],
1: [1 - temperature (numeric)],
2: [2 - humidity (numeric)],
3: [3 - windy (nominal)],
4: [4 - play (nominal)]} | {'MajorityClassSize': 9.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 5.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 14.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 3.0,
'cost_m... | Weather | [
"outlook",
"temperature",
"humidity",
"windy"
] | [
true,
false,
false,
true
] | 3,454 |
363,112 | predictive_accuracy | accuracy_score | Weather | The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some unspecified game. In general, instances in a dataset are characterized by the values of features, or attributes, that me... | {0: [0 - outlook (nominal)],
1: [1 - temperature (numeric)],
2: [2 - humidity (numeric)],
3: [3 - windy (nominal)],
4: [4 - play (nominal)]} | {'MajorityClassSize': 9.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 5.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 14.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 3.0,
'cost_m... | Weather | [
"outlook",
"temperature",
"humidity",
"windy"
] | [
true,
false,
false,
true
] | 3,455 |
363,104 | predictive_accuracy | accuracy_score | Weather | The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some unspecified game. In general, instances in a dataset are characterized by the values of features, or attributes, that me... | {0: [0 - outlook (nominal)],
1: [1 - temperature (numeric)],
2: [2 - humidity (numeric)],
3: [3 - windy (nominal)],
4: [4 - play (nominal)]} | {'MajorityClassSize': 9.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 5.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 14.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 3.0,
'cost_m... | Weather | [
"outlook",
"temperature",
"humidity",
"windy"
] | [
true,
false,
false,
true
] | 3,456 |
361,860 | predictive_accuracy | accuracy_score | timing-attack-dataset-15-micro-seconds-delay-2022-09-21 | Bleichenbacher Timing Attack: 15 micro seconds dataset created on 2022-09-21
Attribute Descriptions:
CCS0:tcp.srcport: TCP Source Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.dstport: TCP Destination Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.port: TCP Source or Destination ... | {0: [0 - label (string)],
1: [1 - CCS0:tcp.srcport (numeric)],
2: [2 - CCS0:tcp.dstport (numeric)],
3: [3 - CCS0:tcp.port (numeric)],
4: [4 - CCS0:tcp.stream (numeric)],
5: [5 - CCS0:tcp.len (numeric)],
6: [6 - CCS0:tcp.seq (numeric)],
7: [7 - CCS0:tcp.nxtseq (numeric)],
8: [8 - CCS0:tcp.ack (numeric)],
9: [9 ... | {'MajorityClassSize': 957.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 859.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9996.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-15-micro-seconds-delay-2022-09-21 | [
"CCS0:tcp.srcport",
"CCS0:tcp.dstport",
"CCS0:tcp.port",
"CCS0:tcp.stream",
"CCS0:tcp.len",
"CCS0:tcp.seq",
"CCS0:tcp.nxtseq",
"CCS0:tcp.ack",
"CCS0:tcp.hdr_len",
"CCS0:tcp.flags.res",
"CCS0:tcp.flags.ns",
"CCS0:tcp.flags.cwr",
"CCS0:tcp.flags.ecn",
"CCS0:tcp.flags.urg",
"CCS0:tcp.flags.... | [
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... | 3,458 |
363,106 | predictive_accuracy | accuracy_score | Weather | The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some unspecified game. In general, instances in a dataset are characterized by the values of features, or attributes, that me... | {0: [0 - outlook (nominal)],
1: [1 - temperature (numeric)],
2: [2 - humidity (numeric)],
3: [3 - windy (nominal)],
4: [4 - play (nominal)]} | {'MajorityClassSize': 9.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 5.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 14.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 3.0,
'cost_m... | Weather | [
"outlook",
"temperature",
"humidity",
"windy"
] | [
true,
false,
false,
true
] | 3,459 |
361,819 | predictive_accuracy | accuracy_score | timing-attack-dataset-2-micro-seconds-delay-2022-09-17 | Bleichenbacher Timing Attack: 2 micro seconds dataset created on 2022-09-17
Attribute Descriptions:
CCS0:tcp.srcport: TCP Source Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.dstport: TCP Destination Port of the first Change Cipher Spec TCP Acknowledgement
CCS0:tcp.port: TCP Source or Destination P... | {0: [0 - label (string)],
1: [1 - CCS0:tcp.srcport (numeric)],
2: [2 - CCS0:tcp.dstport (numeric)],
3: [3 - CCS0:tcp.port (numeric)],
4: [4 - CCS0:tcp.stream (numeric)],
5: [5 - CCS0:tcp.len (numeric)],
6: [6 - CCS0:tcp.seq (numeric)],
7: [7 - CCS0:tcp.nxtseq (numeric)],
8: [8 - CCS0:tcp.ack (numeric)],
9: [9 ... | {'MajorityClassSize': 947.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 875.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9994.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-2-micro-seconds-delay-2022-09-17 | [
"CCS0:tcp.srcport",
"CCS0:tcp.dstport",
"CCS0:tcp.port",
"CCS0:tcp.stream",
"CCS0:tcp.len",
"CCS0:tcp.seq",
"CCS0:tcp.nxtseq",
"CCS0:tcp.ack",
"CCS0:tcp.hdr_len",
"CCS0:tcp.flags.res",
"CCS0:tcp.flags.ns",
"CCS0:tcp.flags.cwr",
"CCS0:tcp.flags.ecn",
"CCS0:tcp.flags.urg",
"CCS0:tcp.flags.... | [
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... | 3,460 |
363,117 | predictive_accuracy | accuracy_score | Weather | The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some unspecified game. In general, instances in a dataset are characterized by the values of features, or attributes, that me... | {0: [0 - outlook (nominal)],
1: [1 - temperature (numeric)],
2: [2 - humidity (numeric)],
3: [3 - windy (nominal)],
4: [4 - play (nominal)]} | {'MajorityClassSize': 9.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 5.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 14.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 3.0,
'cost_m... | Weather | [
"outlook",
"temperature",
"humidity",
"windy"
] | [
true,
false,
false,
true
] | 3,461 |
363,029 | predictive_accuracy | accuracy_score | porto-seguro_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset porto-seguro (42742) with
seed=1
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max:... | {0: [0 - ps_ind_01 (numeric)],
1: [1 - ps_ind_02_cat (nominal)],
2: [2 - ps_ind_03 (numeric)],
3: [3 - ps_ind_04_cat (nominal)],
4: [4 - ps_ind_05_cat (nominal)],
5: [5 - ps_ind_06_bin (nominal)],
6: [6 - ps_ind_07_bin (nominal)],
7: [7 - ps_ind_08_bin (nominal)],
8: [8 - ps_ind_09_bin (nominal)],
9: [9 - ps_i... | {'MajorityClassSize': 1927.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 73.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 58.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 1545.0,
'NumberOfMissingValues': 2775.0,
'NumberOfNumericFeatures': 26.0,
'NumberOfSymbolicFeatures':... | porto-seguro_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"ps_ind_01",
"ps_ind_02_cat",
"ps_ind_03",
"ps_ind_04_cat",
"ps_ind_05_cat",
"ps_ind_06_bin",
"ps_ind_07_bin",
"ps_ind_08_bin",
"ps_ind_09_bin",
"ps_ind_10_bin",
"ps_ind_11_bin",
"ps_ind_12_bin",
"ps_ind_13_bin",
"ps_ind_14",
"ps_ind_15",
"ps_ind_16_bin",
"ps_ind_17_bin",
"ps_ind_1... | [
false,
true,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
true,
true,
true,
false,
false,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
false,
false,
false,
false,
... | 3,462 |
362,947 | predictive_accuracy | accuracy_score | Fashion-MNIST_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Fashion-MNIST (40996) with
seed=4
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max... | {0: [0 - pixel21 (numeric)],
1: [1 - pixel28 (numeric)],
2: [2 - pixel43 (numeric)],
3: [3 - pixel56 (numeric)],
4: [4 - pixel60 (numeric)],
5: [5 - pixel66 (numeric)],
6: [6 - pixel88 (numeric)],
7: [7 - pixel98 (numeric)],
8: [8 - pixel100 (numeric)],
9: [9 - pixel123 (numeric)],
10: [10 - pixel129 (numeric... | {'MajorityClassSize': 200.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 200.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | Fashion-MNIST_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"pixel21",
"pixel28",
"pixel43",
"pixel56",
"pixel60",
"pixel66",
"pixel88",
"pixel98",
"pixel100",
"pixel123",
"pixel129",
"pixel133",
"pixel138",
"pixel147",
"pixel150",
"pixel152",
"pixel155",
"pixel166",
"pixel167",
"pixel173",
"pixel197",
"pixel202",
"pixel221",
"p... | [
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... | 3,463 |
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