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 |
|---|---|---|---|---|---|---|---|---|---|---|
363,114 | 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
] | 3,464 |
363,028 | predictive_accuracy | accuracy_score | porto-seguro_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset porto-seguro (42742) 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 - 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': 1577.0,
'NumberOfMissingValues': 2846.0,
'NumberOfNumericFeatures': 26.0,
'NumberOfSymbolicFeatures':... | porto-seguro_seed_0_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,
... | 3,465 |
361,831 | predictive_accuracy | accuracy_score | timing-attack-dataset-32-micro-seconds-delay-2022-09-17 | Bleichenbacher Timing Attack: 32 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': 953.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 863.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9997.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-32-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... | 3,466 |
363,108 | predictive_accuracy | accuracy_score | dummy | randomly create description | {0: [0 - y (nominal)],
1: [1 - X0 (numeric)],
2: [2 - X1 (numeric)],
3: [3 - X2 (numeric)],
4: [4 - X3 (numeric)],
5: [5 - X4 (numeric)],
6: [6 - X5 (numeric)]} | {'MajorityClassSize': 727.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 273.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 1.0,
'... | dummy | [
"X0",
"X1",
"X2",
"X3",
"X4",
"X5"
] | [
false,
false,
false,
false,
false,
false
] | 3,467 |
363,030 | predictive_accuracy | accuracy_score | porto-seguro_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset porto-seguro (42742) 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 - 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': 1583.0,
'NumberOfMissingValues': 2863.0,
'NumberOfNumericFeatures': 26.0,
'NumberOfSymbolicFeatures':... | porto-seguro_seed_3_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,
... | 3,468 |
361,820 | predictive_accuracy | accuracy_score | timing-attack-dataset-4-micro-seconds-delay-2022-09-01 | Bleichenbacher Timing Attack: 4 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': 956.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 836.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9995.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-4-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... | 3,469 |
361,837 | predictive_accuracy | accuracy_score | timing-attack-dataset-128-micro-seconds-delay-2022-09-17 | Bleichenbacher Timing Attack: 128 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': 956.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 880.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9995.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-128-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... | 3,470 |
362,960 | predictive_accuracy | accuracy_score | nomao_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset nomao (1486) 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 - V2 (numeric)],
1: [1 - V3 (numeric)],
2: [2 - V5 (numeric)],
3: [3 - V6 (numeric)],
4: [4 - V7 (nominal)],
5: [5 - V8 (nominal)],
6: [6 - V9 (numeric)],
7: [7 - V10 (numeric)],
8: [8 - V11 (numeric)],
9: [9 - V12 (numeric)],
10: [10 - V16 (nominal)],
11: [11 - V17 (numeric)],
12: [12 - V20 (numeric... | {'MajorityClassSize': 1429.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 571.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 75.0,
'NumberOfSymbolicFeatures': 26.... | nomao_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V2",
"V3",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V16",
"V17",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27",
"V30",
"V31",
"V32",
"V33",
"V34",
"V35",
"V36",
"V37",
"V38",
"V39",
"V41",
"V42",
"V43",
"V44",
"V45",
"V46",
... | [
false,
false,
false,
false,
true,
true,
false,
false,
false,
false,
true,
false,
false,
false,
false,
true,
true,
false,
false,
false,
false,
true,
true,
false,
false,
false,
false,
false,
false,
true,
false,
false,
false,
false,
false,
false,
... | 3,471 |
362,932 | predictive_accuracy | accuracy_score | christine_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset christine (41142) 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 - V44 (numeric)],
1: [1 - V57 (numeric)],
2: [2 - V95 (numeric)],
3: [3 - V125 (numeric)],
4: [4 - V128 (numeric)],
5: [5 - V143 (numeric)],
6: [6 - V187 (numeric)],
7: [7 - V209 (numeric)],
8: [8 - V212 (numeric)],
9: [9 - V218 (numeric)],
10: [10 - V264 (numeric)],
11: [11 - V271 (numeric)],
12: [1... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 99.0,
'NumberOfSymbolicFeatures': 2.... | christine_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V44",
"V57",
"V95",
"V125",
"V128",
"V143",
"V187",
"V209",
"V212",
"V218",
"V264",
"V271",
"V278",
"V280",
"V291",
"V310",
"V321",
"V329",
"V342",
"V351",
"V358",
"V361",
"V431",
"V439",
"V480",
"V527",
"V558",
"V580",
"V583",
"V589",
"V594",
"V626",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,472 |
363,033 | predictive_accuracy | accuracy_score | porto-seguro_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset porto-seguro (42742) 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 - 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': 1576.0,
'NumberOfMissingValues': 2837.0,
'NumberOfNumericFeatures': 26.0,
'NumberOfSymbolicFeatures':... | porto-seguro_seed_4_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,
... | 3,473 |
361,867 | predictive_accuracy | accuracy_score | timing-attack-dataset-20-micro-seconds-delay-2022-09-18 | Bleichenbacher Timing Attack: 20 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': 963.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 864.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9995.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-20-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... | 3,474 |
362,961 | predictive_accuracy | accuracy_score | nomao_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset nomao (1486) 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 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V8 (nominal)],
7: [7 - V9 (numeric)],
8: [8 - V10 (numeric)],
9: [9 - V12 (numeric)],
10: [10 - V13 (numeric)],
11: [11 - V14 (numeric)],
12: [12 - V15 (nominal)... | {'MajorityClassSize': 1429.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 571.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 74.0,
'NumberOfSymbolicFeatures': 27.... | nomao_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V8",
"V9",
"V10",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27",
"V28",
"V30",
"V31",
"V32",
"V34",
"V36",
"V37",
"V38",
"V39",
"V40",
"V41",
... | [
false,
false,
false,
false,
false,
false,
true,
false,
false,
false,
false,
false,
true,
true,
false,
false,
false,
false,
false,
false,
true,
true,
false,
false,
false,
false,
false,
true,
true,
false,
false,
false,
false,
true,
true,
false,
f... | 3,475 |
362,929 | predictive_accuracy | accuracy_score | christine_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset christine (41142) 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 - V32 (numeric)],
1: [1 - V43 (numeric)],
2: [2 - V54 (numeric)],
3: [3 - V64 (numeric)],
4: [4 - V89 (numeric)],
5: [5 - V98 (numeric)],
6: [6 - V101 (numeric)],
7: [7 - V134 (numeric)],
8: [8 - V147 (numeric)],
9: [9 - V186 (numeric)],
10: [10 - V194 (numeric)],
11: [11 - V195 (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': 96.0,
'NumberOfSymbolicFeatures': 5.... | christine_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V32",
"V43",
"V54",
"V64",
"V89",
"V98",
"V101",
"V134",
"V147",
"V186",
"V194",
"V195",
"V211",
"V223",
"V242",
"V257",
"V321",
"V349",
"V386",
"V399",
"V414",
"V423",
"V424",
"V441",
"V444",
"V466",
"V469",
"V475",
"V483",
"V515",
"V516",
"V561",
"V... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
fa... | 3,476 |
363,122 | predictive_accuracy | accuracy_score | schizo | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Schizophrenic Eye-Tracking Data in Rubin and Wu (1997)
Biometrics. Yingnian Wu (wu@hustat.harvard.edu) [14/Oct/97]
Information about the dataset
CLASSTYPE: nominal
CLASSINDEX: last | {0: [0 - ID (numeric)],
1: [1 - target (nominal)],
2: [2 - gain_ratio_1 (numeric)],
3: [3 - gain_ratio_2 (numeric)],
4: [4 - gain_ratio_3 (numeric)],
5: [5 - gain_ratio_4 (numeric)],
6: [6 - gain_ratio_5 (numeric)],
7: [7 - gain_ratio_6 (numeric)],
8: [8 - gain_ratio_7 (numeric)],
9: [9 - gain_ratio_8 (numeric... | {'MajorityClassSize': 177.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 163.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 15.0,
'NumberOfInstances': 340.0,
'NumberOfInstancesWithMissingValues': 228.0,
'NumberOfMissingValues': 834.0,
'NumberOfNumericFeatures': 12.0,
'NumberOfSymbolicFeatures': 3.... | schizo | [
"target",
"gain_ratio_1",
"gain_ratio_2",
"gain_ratio_3",
"gain_ratio_4",
"gain_ratio_5",
"gain_ratio_6",
"gain_ratio_7",
"gain_ratio_8",
"gain_ratio_9",
"gain_ratio_10",
"gain_ratio_11",
"sex"
] | [
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 3,477 |
363,123 | predictive_accuracy | accuracy_score | teachingAssistant | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Dataset from the MLRR repository: http://axon.cs.byu.edu:5000/ | {0: [0 - ID (numeric)],
1: [1 - EnglishSepaker (nominal)],
2: [2 - courseInstructor (nominal)],
3: [3 - course (nominal)],
4: [4 - summer (nominal)],
5: [5 - classSize (numeric)],
6: [6 - class (nominal)]} | {'MajorityClassSize': 52.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 49.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 6.0,
'NumberOfInstances': 151.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 5.0,
'cos... | teachingAssistant | [
"EnglishSepaker",
"courseInstructor",
"course",
"summer",
"classSize"
] | [
true,
true,
true,
true,
false
] | 3,478 |
363,034 | predictive_accuracy | accuracy_score | KDDCup09-Upselling_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset KDDCup09-Upselling (43072) 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,
nclasse... | {0: [0 - Var298 (numeric)],
1: [1 - Var412 (numeric)],
2: [2 - Var519 (numeric)],
3: [3 - Var592 (numeric)],
4: [4 - Var809 (numeric)],
5: [5 - Var924 (numeric)],
6: [6 - Var931 (numeric)],
7: [7 - Var1274 (numeric)],
8: [8 - Var1373 (numeric)],
9: [9 - Var1727 (numeric)],
10: [10 - Var1741 (numeric)],
11: [... | {'MajorityClassSize': 1853.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 147.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 2000.0,
'NumberOfMissingValues': 7121.0,
'NumberOfNumericFeatures': 99.0,
'NumberOfSymbolicFeatures... | KDDCup09-Upselling_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Var298",
"Var412",
"Var519",
"Var592",
"Var809",
"Var924",
"Var931",
"Var1274",
"Var1373",
"Var1727",
"Var1741",
"Var1842",
"Var1850",
"Var1993",
"Var2140",
"Var2209",
"Var2392",
"Var3025",
"Var3205",
"Var3698",
"Var3815",
"Var3879",
"Var3900",
"Var4053",
"Var4121",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,479 |
362,959 | predictive_accuracy | accuracy_score | nomao_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset nomao (1486) 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 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V4 (numeric)],
3: [3 - V6 (numeric)],
4: [4 - V7 (nominal)],
5: [5 - V8 (nominal)],
6: [6 - V9 (numeric)],
7: [7 - V10 (numeric)],
8: [8 - V11 (numeric)],
9: [9 - V12 (numeric)],
10: [10 - V13 (numeric)],
11: [11 - V14 (numeric)],
12: [12 - V15 (nominal... | {'MajorityClassSize': 1429.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 571.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 77.0,
'NumberOfSymbolicFeatures': 24.... | nomao_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V4",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V18",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27",
"V28",
"V29",
"V31",
"V32",
"V33",
"V34",
"V35",
"V36",
"V37",
"V39",
"V40",
"V42",
"V45",
... | [
false,
false,
false,
false,
true,
true,
false,
false,
false,
false,
false,
false,
true,
true,
false,
false,
false,
false,
true,
true,
false,
false,
false,
false,
false,
true,
true,
false,
false,
false,
false,
false,
true,
true,
false,
false,
fa... | 3,481 |
363,031 | predictive_accuracy | accuracy_score | porto-seguro_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset porto-seguro (42742) 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 - 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': 1577.0,
'NumberOfMissingValues': 2855.0,
'NumberOfNumericFeatures': 26.0,
'NumberOfSymbolicFeatures':... | porto-seguro_seed_2_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,
... | 3,482 |
362,958 | predictive_accuracy | accuracy_score | nomao_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset nomao (1486) 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 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V5 (numeric)],
3: [3 - V6 (numeric)],
4: [4 - V7 (nominal)],
5: [5 - V8 (nominal)],
6: [6 - V9 (numeric)],
7: [7 - V10 (numeric)],
8: [8 - V11 (numeric)],
9: [9 - V12 (numeric)],
10: [10 - V13 (numeric)],
11: [11 - V16 (nominal)],
12: [12 - V17 (numeric... | {'MajorityClassSize': 1429.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 571.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 75.0,
'NumberOfSymbolicFeatures': 26.... | nomao_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V23",
"V24",
"V25",
"V26",
"V27",
"V28",
"V29",
"V30",
"V32",
"V33",
"V34",
"V35",
"V36",
"V38",
"V39",
"V40",
"V41",
"V42",
"V43",
... | [
false,
false,
false,
false,
true,
true,
false,
false,
false,
false,
false,
true,
false,
false,
false,
false,
false,
true,
true,
false,
false,
false,
false,
false,
false,
true,
false,
false,
false,
false,
false,
true,
true,
false,
false,
false,
... | 3,483 |
361,877 | predictive_accuracy | accuracy_score | timing-attack-dataset-25-micro-seconds-delay-2022-09-18 | Bleichenbacher Timing Attack: 25 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': 949.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 861.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9996.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-25-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... | 3,484 |
361,827 | predictive_accuracy | accuracy_score | timing-attack-dataset-16-micro-seconds-delay-2022-09-12 | Bleichenbacher Timing Attack: 16 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': 959.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 843.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9998.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-16-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... | 3,485 |
361,848 | predictive_accuracy | accuracy_score | timing-attack-dataset-10-micro-seconds-delay-2022-09-19 | Bleichenbacher Timing Attack: 10 micro seconds dataset created on 2022-09-19
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': 942.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 865.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9999.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-10-micro-seconds-delay-2022-09-19 | [
"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... | 3,486 |
361,832 | predictive_accuracy | accuracy_score | timing-attack-dataset-64-micro-seconds-delay-2022-09-01 | Bleichenbacher Timing Attack: 64 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': 972.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 856.0,
'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-2022-09-01 | [
"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... | 3,487 |
363,018 | predictive_accuracy | accuracy_score | sf-police-incidents_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset sf-police-incidents (42732) 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,
nclass... | {0: [0 - Hour (numeric)],
1: [1 - DayOfWeek (nominal)],
2: [2 - Month (nominal)],
3: [3 - Year (nominal)],
4: [4 - PdDistrict (nominal)],
5: [5 - Address (nominal)],
6: [6 - X (numeric)],
7: [7 - Y (numeric)],
8: [8 - ViolentCrime (nominal)]} | {'MajorityClassSize': 1757.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 243.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 6.0,
... | sf-police-incidents_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Hour",
"DayOfWeek",
"Month",
"Year",
"PdDistrict",
"Address",
"X",
"Y"
] | [
false,
true,
true,
true,
true,
true,
false,
false
] | 3,488 |
363,128 | predictive_accuracy | accuracy_score | cylinder-bands | **Author**: Bob Evans, RR Donnelley & Sons Co.
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Cylinder+Bands) - August, 1995
**Please cite**: [UCI citation policy](https://archive.ics.uci.edu/ml/citation_policy.html)
### Description
Cylinder bands UCI dataset - Process delays known as cylinder banding... | {0: [0 - timestamp (nominal)],
1: [1 - cylinder_number (nominal)],
2: [2 - customer (nominal)],
3: [3 - job_number (numeric)],
4: [4 - grain_screened (nominal)],
5: [5 - ink_color (nominal)],
6: [6 - proof_on_ctd_ink (nominal)],
7: [7 - blade_mfg (nominal)],
8: [8 - cylinder_division (nominal)],
9: [9 - paper_... | {'MajorityClassSize': 312.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 228.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 38.0,
'NumberOfInstances': 540.0,
'NumberOfInstancesWithMissingValues': 263.0,
'NumberOfMissingValues': 999.0,
'NumberOfNumericFeatures': 18.0,
'NumberOfSymbolicFeatures': 20... | cylinder-bands | [
"customer",
"job_number",
"grain_screened",
"ink_color",
"proof_on_ctd_ink",
"blade_mfg",
"cylinder_division",
"paper_type",
"ink_type",
"direct_steam",
"solvent_type",
"type_on_cylinder",
"press_type",
"press",
"unit_number",
"cylinder_size",
"paper_mill_location",
"plating_tank",... | [
true,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
true,
true,
true,
false,
false,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true,
false,
true
] | 3,489 |
361,871 | predictive_accuracy | accuracy_score | timing-attack-dataset-25-micro-seconds-delay-2022-09-04 | Bleichenbacher Timing Attack: 25 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': 981.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 855.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9994.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-25-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... | 3,490 |
363,260 | predictive_accuracy | accuracy_score | metafeaturesarff | meta features with best models | {0: [0 - DatasetRatio (numeric)],
1: [1 - InverseDatasetRatio (numeric)],
2: [2 - KurtosisMax (numeric)],
3: [3 - KurtosisMean (numeric)],
4: [4 - KurtosisMin (numeric)],
5: [5 - KurtosisSTD (numeric)],
6: [6 - LogDatasetRatio (numeric)],
7: [7 - LogInverseDatasetRatio (numeric)],
8: [8 - LogNumberOfFeatures (n... | {'MajorityClassSize': 22.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 2.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 32.0,
'NumberOfInstances': 75.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 31.0,
'NumberOfSymbolicFeatures': 1.0,
'co... | metafeaturesarff | [
"DatasetRatio",
"InverseDatasetRatio",
"KurtosisMax",
"KurtosisMean",
"KurtosisMin",
"KurtosisSTD",
"LogDatasetRatio",
"LogInverseDatasetRatio",
"LogNumberOfFeatures",
"LogNumberOfInstances",
"NumberOfCategoricalFeatures",
"NumberOfFeatures",
"NumberOfFeaturesWithMissingValues",
"NumberOfI... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,491 |
361,890 | predictive_accuracy | accuracy_score | timing-attack-dataset-30-micro-seconds-delay-2022-09-21 | Bleichenbacher Timing Attack: 30 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': 956.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 865.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9993.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-30-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... | 3,492 |
363,121 | predictive_accuracy | accuracy_score | cylinder-bands | **Author**: Bob Evans, RR Donnelley & Sons Co.
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Cylinder+Bands) - August, 1995
**Please cite**: [UCI citation policy](https://archive.ics.uci.edu/ml/citation_policy.html)
### Description
Cylinder bands UCI dataset - Process delays known as cylinder banding... | {0: [0 - timestamp (nominal)],
1: [1 - cylinder_number (nominal)],
2: [2 - customer (nominal)],
3: [3 - job_number (numeric)],
4: [4 - grain_screened (nominal)],
5: [5 - ink_color (nominal)],
6: [6 - proof_on_ctd_ink (nominal)],
7: [7 - blade_mfg (nominal)],
8: [8 - cylinder_division (nominal)],
9: [9 - paper_... | {'MajorityClassSize': 312.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 228.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 38.0,
'NumberOfInstances': 540.0,
'NumberOfInstancesWithMissingValues': 263.0,
'NumberOfMissingValues': 999.0,
'NumberOfNumericFeatures': 18.0,
'NumberOfSymbolicFeatures': 20... | cylinder-bands | [
"timestamp",
"cylinder_number",
"customer",
"grain_screened",
"ink_color",
"proof_on_ctd_ink",
"blade_mfg",
"cylinder_division",
"paper_type",
"ink_type",
"direct_steam",
"solvent_type",
"type_on_cylinder",
"press_type",
"press",
"unit_number",
"cylinder_size",
"paper_mill_location... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
true,
true,
true,
false,
false,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true,
false,
tru... | 3,494 |
361,834 | predictive_accuracy | accuracy_score | timing-attack-dataset-64-micro-seconds-delay-2022-09-17 | Bleichenbacher Timing Attack: 64 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': 958.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 872.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9993.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-64-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... | 3,495 |
361,835 | predictive_accuracy | accuracy_score | timing-attack-dataset-128-micro-seconds-delay-2022-09-01 | Bleichenbacher Timing Attack: 128 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': 966.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 868.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9995.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-128-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... | 3,496 |
363,024 | predictive_accuracy | accuracy_score | sf-police-incidents_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset sf-police-incidents (42732) 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,
nclass... | {0: [0 - Hour (numeric)],
1: [1 - DayOfWeek (nominal)],
2: [2 - Month (nominal)],
3: [3 - Year (nominal)],
4: [4 - PdDistrict (nominal)],
5: [5 - Address (nominal)],
6: [6 - X (numeric)],
7: [7 - Y (numeric)],
8: [8 - ViolentCrime (nominal)]} | {'MajorityClassSize': 1757.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 243.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 6.0,
... | sf-police-incidents_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Hour",
"DayOfWeek",
"Month",
"Year",
"PdDistrict",
"Address",
"X",
"Y"
] | [
false,
true,
true,
true,
true,
true,
false,
false
] | 3,497 |
361,878 | predictive_accuracy | accuracy_score | timing-attack-dataset-25-micro-seconds-delay-2022-09-19 | Bleichenbacher Timing Attack: 25 micro seconds dataset created on 2022-09-19
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': 958.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 881.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9996.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-25-micro-seconds-delay-2022-09-19 | [
"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... | 3,498 |
361,838 | predictive_accuracy | accuracy_score | timing-attack-dataset-256-micro-seconds-delay-2022-09-01 | Bleichenbacher Timing Attack: 256 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': 989.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 850.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9999.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-256-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... | 3,499 |
363,019 | predictive_accuracy | accuracy_score | sf-police-incidents_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset sf-police-incidents (42732) 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,
nclass... | {0: [0 - Hour (numeric)],
1: [1 - DayOfWeek (nominal)],
2: [2 - Month (nominal)],
3: [3 - Year (nominal)],
4: [4 - PdDistrict (nominal)],
5: [5 - Address (nominal)],
6: [6 - X (numeric)],
7: [7 - Y (numeric)],
8: [8 - ViolentCrime (nominal)]} | {'MajorityClassSize': 1757.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 243.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 6.0,
... | sf-police-incidents_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Hour",
"DayOfWeek",
"Month",
"Year",
"PdDistrict",
"Address",
"X",
"Y"
] | [
false,
true,
true,
true,
true,
true,
false,
false
] | 3,500 |
363,010 | predictive_accuracy | accuracy_score | okcupid-stem_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset okcupid-stem (42734) 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 - age (numeric)],
1: [1 - body_type (nominal)],
2: [2 - diet (nominal)],
3: [3 - drinks (nominal)],
4: [4 - drugs (nominal)],
5: [5 - education (nominal)],
6: [6 - ethnicity (nominal)],
7: [7 - height (numeric)],
8: [8 - income (nominal)],
9: [9 - location (nominal)],
10: [10 - offspring (nominal)],
1... | {'MajorityClassSize': 1432.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 192.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 20.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 1922.0,
'NumberOfMissingValues': 6105.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures':... | okcupid-stem_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"age",
"body_type",
"diet",
"drinks",
"drugs",
"education",
"ethnicity",
"height",
"income",
"location",
"offspring",
"orientation",
"pets",
"religion",
"sex",
"sign",
"smokes",
"speaks",
"status"
] | [
false,
true,
true,
true,
true,
true,
true,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,502 |
363,009 | predictive_accuracy | accuracy_score | okcupid-stem_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset okcupid-stem (42734) 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 - age (numeric)],
1: [1 - body_type (nominal)],
2: [2 - diet (nominal)],
3: [3 - drinks (nominal)],
4: [4 - drugs (nominal)],
5: [5 - education (nominal)],
6: [6 - ethnicity (nominal)],
7: [7 - height (numeric)],
8: [8 - income (nominal)],
9: [9 - location (nominal)],
10: [10 - offspring (nominal)],
1... | {'MajorityClassSize': 1432.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 192.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 20.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 1926.0,
'NumberOfMissingValues': 6117.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures':... | okcupid-stem_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"age",
"body_type",
"diet",
"drinks",
"drugs",
"education",
"ethnicity",
"height",
"income",
"location",
"offspring",
"orientation",
"pets",
"religion",
"sex",
"sign",
"smokes",
"speaks",
"status"
] | [
false,
true,
true,
true,
true,
true,
true,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,503 |
363,008 | predictive_accuracy | accuracy_score | okcupid-stem_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset okcupid-stem (42734) 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 - age (numeric)],
1: [1 - body_type (nominal)],
2: [2 - diet (nominal)],
3: [3 - drinks (nominal)],
4: [4 - drugs (nominal)],
5: [5 - education (nominal)],
6: [6 - ethnicity (nominal)],
7: [7 - height (numeric)],
8: [8 - income (nominal)],
9: [9 - location (nominal)],
10: [10 - offspring (nominal)],
1... | {'MajorityClassSize': 1432.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 192.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 20.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 1890.0,
'NumberOfMissingValues': 5980.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures':... | okcupid-stem_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"age",
"body_type",
"diet",
"drinks",
"drugs",
"education",
"ethnicity",
"height",
"income",
"location",
"offspring",
"orientation",
"pets",
"religion",
"sex",
"sign",
"smokes",
"speaks",
"status"
] | [
false,
true,
true,
true,
true,
true,
true,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,504 |
363,129 | predictive_accuracy | accuracy_score | cylinder-bands | **Author**: Bob Evans, RR Donnelley & Sons Co.
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Cylinder+Bands) - August, 1995
**Please cite**: [UCI citation policy](https://archive.ics.uci.edu/ml/citation_policy.html)
### Description
Cylinder bands UCI dataset - Process delays known as cylinder banding... | {0: [0 - timestamp (nominal)],
1: [1 - cylinder_number (nominal)],
2: [2 - customer (nominal)],
3: [3 - job_number (numeric)],
4: [4 - grain_screened (nominal)],
5: [5 - ink_color (nominal)],
6: [6 - proof_on_ctd_ink (nominal)],
7: [7 - blade_mfg (nominal)],
8: [8 - cylinder_division (nominal)],
9: [9 - paper_... | {'MajorityClassSize': 312.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 228.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 38.0,
'NumberOfInstances': 540.0,
'NumberOfInstancesWithMissingValues': 263.0,
'NumberOfMissingValues': 999.0,
'NumberOfNumericFeatures': 18.0,
'NumberOfSymbolicFeatures': 20... | cylinder-bands | [
"customer",
"job_number",
"grain_screened",
"ink_color",
"proof_on_ctd_ink",
"blade_mfg",
"cylinder_division",
"paper_type",
"ink_type",
"direct_steam",
"solvent_type",
"type_on_cylinder",
"press_type",
"press",
"unit_number",
"cylinder_size",
"paper_mill_location",
"plating_tank",... | [
true,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
true,
true,
true,
false,
false,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true,
false,
true
] | 3,505 |
361,829 | predictive_accuracy | accuracy_score | timing-attack-dataset-32-micro-seconds-delay-2022-09-01 | Bleichenbacher Timing Attack: 32 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': 949.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 872.0,
'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-2022-09-01 | [
"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... | 3,506 |
363,130 | predictive_accuracy | accuracy_score | cylinder-bands | **Author**: Bob Evans, RR Donnelley & Sons Co.
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Cylinder+Bands) - August, 1995
**Please cite**: [UCI citation policy](https://archive.ics.uci.edu/ml/citation_policy.html)
### Description
Cylinder bands UCI dataset - Process delays known as cylinder banding... | {0: [0 - timestamp (nominal)],
1: [1 - cylinder_number (nominal)],
2: [2 - customer (nominal)],
3: [3 - job_number (numeric)],
4: [4 - grain_screened (nominal)],
5: [5 - ink_color (nominal)],
6: [6 - proof_on_ctd_ink (nominal)],
7: [7 - blade_mfg (nominal)],
8: [8 - cylinder_division (nominal)],
9: [9 - paper_... | {'MajorityClassSize': 312.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 228.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 38.0,
'NumberOfInstances': 540.0,
'NumberOfInstancesWithMissingValues': 263.0,
'NumberOfMissingValues': 999.0,
'NumberOfNumericFeatures': 18.0,
'NumberOfSymbolicFeatures': 20... | cylinder-bands | [
"customer",
"grain_screened",
"ink_color",
"proof_on_ctd_ink",
"blade_mfg",
"cylinder_division",
"paper_type",
"ink_type",
"direct_steam",
"solvent_type",
"type_on_cylinder",
"press_type",
"press",
"unit_number",
"cylinder_size",
"paper_mill_location",
"plating_tank",
"proof_cut",
... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
true,
true,
true,
false,
false,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true,
false,
true
] | 3,507 |
363,015 | predictive_accuracy | accuracy_score | okcupid-stem_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset okcupid-stem (42734) 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 - age (numeric)],
1: [1 - body_type (nominal)],
2: [2 - diet (nominal)],
3: [3 - drinks (nominal)],
4: [4 - drugs (nominal)],
5: [5 - education (nominal)],
6: [6 - ethnicity (nominal)],
7: [7 - height (numeric)],
8: [8 - income (nominal)],
9: [9 - location (nominal)],
10: [10 - offspring (nominal)],
1... | {'MajorityClassSize': 1432.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 192.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 20.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 1910.0,
'NumberOfMissingValues': 6050.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures':... | okcupid-stem_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"age",
"body_type",
"diet",
"drinks",
"drugs",
"education",
"ethnicity",
"height",
"income",
"location",
"offspring",
"orientation",
"pets",
"religion",
"sex",
"sign",
"smokes",
"speaks",
"status"
] | [
false,
true,
true,
true,
true,
true,
true,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,508 |
361,858 | predictive_accuracy | accuracy_score | timing-attack-dataset-15-micro-seconds-delay-2022-09-19 | Bleichenbacher Timing Attack: 15 micro seconds dataset created on 2022-09-19
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': 951.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 867.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9997.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-15-micro-seconds-delay-2022-09-19 | [
"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... | 3,509 |
363,035 | predictive_accuracy | accuracy_score | KDDCup09-Upselling_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset KDDCup09-Upselling (43072) 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,
nclasse... | {0: [0 - Var578 (numeric)],
1: [1 - Var593 (numeric)],
2: [2 - Var820 (numeric)],
3: [3 - Var850 (numeric)],
4: [4 - Var1143 (numeric)],
5: [5 - Var1366 (numeric)],
6: [6 - Var1445 (numeric)],
7: [7 - Var1505 (numeric)],
8: [8 - Var1558 (numeric)],
9: [9 - Var1597 (numeric)],
10: [10 - Var1623 (numeric)],
11... | {'MajorityClassSize': 1853.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 147.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 2000.0,
'NumberOfMissingValues': 5914.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeature... | KDDCup09-Upselling_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Var578",
"Var593",
"Var820",
"Var850",
"Var1143",
"Var1366",
"Var1445",
"Var1505",
"Var1558",
"Var1597",
"Var1623",
"Var2230",
"Var2785",
"Var2789",
"Var2990",
"Var3006",
"Var3177",
"Var3227",
"Var3265",
"Var3330",
"Var3862",
"Var3864",
"Var3880",
"Var4082",
"Var4428... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,510 |
363,016 | predictive_accuracy | accuracy_score | okcupid-stem_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset okcupid-stem (42734) 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 - age (numeric)],
1: [1 - body_type (nominal)],
2: [2 - diet (nominal)],
3: [3 - drinks (nominal)],
4: [4 - drugs (nominal)],
5: [5 - education (nominal)],
6: [6 - ethnicity (nominal)],
7: [7 - height (numeric)],
8: [8 - income (nominal)],
9: [9 - location (nominal)],
10: [10 - offspring (nominal)],
1... | {'MajorityClassSize': 1432.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 192.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 20.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 1912.0,
'NumberOfMissingValues': 5992.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures':... | okcupid-stem_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"age",
"body_type",
"diet",
"drinks",
"drugs",
"education",
"ethnicity",
"height",
"income",
"location",
"offspring",
"orientation",
"pets",
"religion",
"sex",
"sign",
"smokes",
"speaks",
"status"
] | [
false,
true,
true,
true,
true,
true,
true,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,511 |
363,134 | predictive_accuracy | accuracy_score | MABL | A brief description of your dataset. | {0: [0 - feature1 (numeric)],
1: [1 - feature2 (numeric)],
2: [2 - class (string)]} | {'MajorityClassSize': 1.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 3.0,
'NumberOfInstances': 3.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_ma... | MABL | [
"feature1",
"feature2"
] | [
false,
false
] | 3,512 |
363,032 | predictive_accuracy | accuracy_score | KDDCup09-Upselling_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset KDDCup09-Upselling (43072) 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,
nclasse... | {0: [0 - Var41 (numeric)],
1: [1 - Var81 (numeric)],
2: [2 - Var124 (numeric)],
3: [3 - Var247 (numeric)],
4: [4 - Var331 (numeric)],
5: [5 - Var424 (numeric)],
6: [6 - Var501 (numeric)],
7: [7 - Var610 (numeric)],
8: [8 - Var726 (numeric)],
9: [9 - Var1088 (numeric)],
10: [10 - Var1118 (numeric)],
11: [11 -... | {'MajorityClassSize': 1853.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 147.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 2000.0,
'NumberOfMissingValues': 3570.0,
'NumberOfNumericFeatures': 99.0,
'NumberOfSymbolicFeatures... | KDDCup09-Upselling_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Var41",
"Var81",
"Var124",
"Var247",
"Var331",
"Var424",
"Var501",
"Var610",
"Var726",
"Var1088",
"Var1118",
"Var1197",
"Var1255",
"Var1329",
"Var1849",
"Var2013",
"Var2601",
"Var2611",
"Var3390",
"Var3797",
"Var3828",
"Var3938",
"Var4001",
"Var4118",
"Var4456",
"V... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,513 |
363,025 | predictive_accuracy | accuracy_score | sf-police-incidents_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset sf-police-incidents (42732) 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,
nclass... | {0: [0 - Hour (numeric)],
1: [1 - DayOfWeek (nominal)],
2: [2 - Month (nominal)],
3: [3 - Year (nominal)],
4: [4 - PdDistrict (nominal)],
5: [5 - Address (nominal)],
6: [6 - X (numeric)],
7: [7 - Y (numeric)],
8: [8 - ViolentCrime (nominal)]} | {'MajorityClassSize': 1757.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 243.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 9.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 6.0,
... | sf-police-incidents_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Hour",
"DayOfWeek",
"Month",
"Year",
"PdDistrict",
"Address",
"X",
"Y"
] | [
false,
true,
true,
true,
true,
true,
false,
false
] | 3,514 |
363,036 | predictive_accuracy | accuracy_score | KDDCup09-Upselling_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset KDDCup09-Upselling (43072) 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,
nclasse... | {0: [0 - Var23 (numeric)],
1: [1 - Var72 (numeric)],
2: [2 - Var454 (numeric)],
3: [3 - Var488 (numeric)],
4: [4 - Var587 (numeric)],
5: [5 - Var643 (numeric)],
6: [6 - Var1145 (numeric)],
7: [7 - Var1272 (numeric)],
8: [8 - Var1355 (numeric)],
9: [9 - Var1399 (numeric)],
10: [10 - Var1529 (numeric)],
11: [1... | {'MajorityClassSize': 1853.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 147.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | KDDCup09-Upselling_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Var23",
"Var72",
"Var454",
"Var488",
"Var587",
"Var643",
"Var1145",
"Var1272",
"Var1355",
"Var1399",
"Var1529",
"Var1689",
"Var2072",
"Var2372",
"Var2569",
"Var2662",
"Var2691",
"Var2833",
"Var3087",
"Var3261",
"Var3358",
"Var3513",
"Var3641",
"Var3695",
"Var3777",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,515 |
361,850 | predictive_accuracy | accuracy_score | timing-attack-dataset-10-micro-seconds-delay-2022-09-21 | Bleichenbacher Timing Attack: 10 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': 946.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 872.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9992.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-10-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... | 3,516 |
361,825 | predictive_accuracy | accuracy_score | timing-attack-dataset-8-micro-seconds-delay-2022-09-17 | Bleichenbacher Timing Attack: 8 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': 934.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 866.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9992.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-8-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... | 3,517 |
363,088 | predictive_accuracy | accuracy_score | FICO-HELOC-cleaned | This dataset is from the "Explainable Machine Learning Challenge":
> The Explainable Machine Learning Challenge is a collaboration between Google, FICO and academics at Berkeley, Oxford, Imperial, UC Irvine and MIT, to generate new research in the area of algorithmic explainability. Teams will be challenged to create ... | {0: [0 - RiskPerformance (nominal)],
1: [1 - ExternalRiskEstimate (numeric)],
2: [2 - MSinceOldestTradeOpen (numeric)],
3: [3 - MSinceMostRecentTradeOpen (numeric)],
4: [4 - AverageMInFile (numeric)],
5: [5 - NumSatisfactoryTrades (numeric)],
6: [6 - NumTrades60Ever2DerogPubRec (numeric)],
7: [7 - NumTrades90Eve... | {'MajorityClassSize': 5136.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 4735.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 24.0,
'NumberOfInstances': 9871.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 21.0,
'NumberOfSymbolicFeatures': 3.0... | FICO-HELOC-cleaned | [
"ExternalRiskEstimate",
"MSinceOldestTradeOpen",
"MSinceMostRecentTradeOpen",
"AverageMInFile",
"NumSatisfactoryTrades",
"NumTrades60Ever2DerogPubRec",
"NumTrades90Ever2DerogPubRec",
"PercentTradesNeverDelq",
"MSinceMostRecentDelq",
"MaxDelq2PublicRecLast12M",
"MaxDelqEver",
"NumTotalTrades",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,518 |
363,144 | predictive_accuracy | accuracy_score | 132 | adas | {0: [0 - V1 (string)],
1: [1 - V2 (string)],
2: [2 - V3 (string)],
3: [3 - Set (string)]} | {'MajorityClassSize': 459.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 443.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 902.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
'c... | 132 | [
"V2",
"V3",
"Set"
] | [
false,
false,
false
] | 3,519 |
362,985 | predictive_accuracy | accuracy_score | robert_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset robert (41165) 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 - V278 (numeric)],
1: [1 - V284 (numeric)],
2: [2 - V393 (numeric)],
3: [3 - V410 (numeric)],
4: [4 - V550 (numeric)],
5: [5 - V654 (numeric)],
6: [6 - V696 (numeric)],
7: [7 - V725 (numeric)],
8: [8 - V749 (numeric)],
9: [9 - V768 (numeric)],
10: [10 - V777 (numeric)],
11: [11 - V1070 (numeric)],
12... | {'MajorityClassSize': 209.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 192.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | robert_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V278",
"V284",
"V393",
"V410",
"V550",
"V654",
"V696",
"V725",
"V749",
"V768",
"V777",
"V1070",
"V1337",
"V1434",
"V1447",
"V1530",
"V1556",
"V1567",
"V1601",
"V1854",
"V1858",
"V1863",
"V1958",
"V2121",
"V2177",
"V2275",
"V2378",
"V2381",
"V2439",
"V2471",... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,520 |
361,851 | predictive_accuracy | accuracy_score | timing-attack-dataset-15-micro-seconds-delay-2022-09-04 | Bleichenbacher Timing Attack: 15 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': 946.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 871.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9997.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-15-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... | 3,521 |
362,983 | predictive_accuracy | accuracy_score | robert_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset robert (41165) 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 - V20 (numeric)],
1: [1 - V39 (numeric)],
2: [2 - V60 (numeric)],
3: [3 - V118 (numeric)],
4: [4 - V158 (numeric)],
5: [5 - V203 (numeric)],
6: [6 - V240 (numeric)],
7: [7 - V292 (numeric)],
8: [8 - V350 (numeric)],
9: [9 - V524 (numeric)],
10: [10 - V535 (numeric)],
11: [11 - V574 (numeric)],
12: [1... | {'MajorityClassSize': 209.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 192.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | robert_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V20",
"V39",
"V60",
"V118",
"V158",
"V203",
"V240",
"V292",
"V350",
"V524",
"V535",
"V574",
"V605",
"V637",
"V888",
"V969",
"V1246",
"V1253",
"V1635",
"V1829",
"V1840",
"V1899",
"V1917",
"V1975",
"V2140",
"V2188",
"V2226",
"V2313",
"V2360",
"V2429",
"V256... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,522 |
361,898 | predictive_accuracy | accuracy_score | timing-attack-dataset-35-micro-seconds-delay-2022-09-19 | Bleichenbacher Timing Attack: 35 micro seconds dataset created on 2022-09-19
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': 943.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 882.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9999.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-35-micro-seconds-delay-2022-09-19 | [
"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... | 3,523 |
363,155 | predictive_accuracy | accuracy_score | Stylized_Meta_Album_AWA_Mini | Mamals dataset for image classification | {0: [0 - FILE_NAME (string)], 1: [1 - CATEGORY (string)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Stylized_Meta_Album_AWA_Mini | [
"FILE_NAME"
] | [
false
] | 3,524 |
363,140 | predictive_accuracy | accuracy_score | StudentsPerformance | This data set consists of the marks secured by the students in various subjects. | {0: [0 - gender (string)],
1: [1 - race/ethnicity (string)],
2: [2 - parental level of education (string)],
3: [3 - lunch (string)],
4: [4 - test preparation course (string)],
5: [5 - math score (numeric)],
6: [6 - reading score (numeric)],
7: [7 - writing score (numeric)]} | {'MajorityClassSize': 518.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 482.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 0.0,
'... | StudentsPerformance | [
"race/ethnicity",
"parental level of education",
"lunch",
"test preparation course",
"math score",
"reading score",
"writing score"
] | [
false,
false,
false,
false,
false,
false,
false
] | 3,525 |
363,002 | predictive_accuracy | accuracy_score | riccardo_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset riccardo (41161) 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 - V115 (numeric)],
1: [1 - V150 (numeric)],
2: [2 - V256 (numeric)],
3: [3 - V338 (numeric)],
4: [4 - V340 (numeric)],
5: [5 - V383 (numeric)],
6: [6 - V493 (numeric)],
7: [7 - V549 (numeric)],
8: [8 - V567 (numeric)],
9: [9 - V589 (numeric)],
10: [10 - V702 (numeric)],
11: [11 - V733 (numeric)],
12:... | {'MajorityClassSize': 1500.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 500.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | riccardo_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V115",
"V150",
"V256",
"V338",
"V340",
"V383",
"V493",
"V549",
"V567",
"V589",
"V702",
"V733",
"V736",
"V753",
"V770",
"V817",
"V855",
"V882",
"V926",
"V928",
"V950",
"V953",
"V1143",
"V1192",
"V1288",
"V1427",
"V1489",
"V1548",
"V1551",
"V1564",
"V1572",... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,526 |
362,978 | predictive_accuracy | accuracy_score | volkert_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset volkert (41166) 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 - V7 (numeric)],
5: [5 - V9 (numeric)],
6: [6 - V10 (numeric)],
7: [7 - V11 (numeric)],
8: [8 - V12 (numeric)],
9: [9 - V15 (numeric)],
10: [10 - V16 (numeric)],
11: [11 - V20 (numeric)],
12: [12 - V23 (numeri... | {'MajorityClassSize': 439.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 47.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0... | volkert_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V7",
"V9",
"V10",
"V11",
"V12",
"V15",
"V16",
"V20",
"V23",
"V27",
"V29",
"V34",
"V36",
"V40",
"V42",
"V43",
"V46",
"V47",
"V48",
"V49",
"V51",
"V52",
"V53",
"V54",
"V56",
"V57",
"V58",
"V60",
"V62",
"V63",
"V65",
"V67",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,527 |
362,981 | predictive_accuracy | accuracy_score | volkert_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset volkert (41166) 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 - V4 (numeric)],
2: [2 - V5 (numeric)],
3: [3 - V6 (numeric)],
4: [4 - V8 (numeric)],
5: [5 - V9 (numeric)],
6: [6 - V10 (numeric)],
7: [7 - V12 (numeric)],
8: [8 - V14 (numeric)],
9: [9 - V15 (numeric)],
10: [10 - V16 (numeric)],
11: [11 - V18 (numeric)],
12: [12 - V19 (numeri... | {'MajorityClassSize': 439.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 47.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0... | volkert_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V4",
"V5",
"V6",
"V8",
"V9",
"V10",
"V12",
"V14",
"V15",
"V16",
"V18",
"V19",
"V20",
"V26",
"V31",
"V32",
"V33",
"V35",
"V36",
"V37",
"V38",
"V40",
"V41",
"V45",
"V46",
"V52",
"V54",
"V57",
"V58",
"V63",
"V64",
"V66",
"V68",
"V69",
"V71",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,528 |
362,987 | predictive_accuracy | accuracy_score | robert_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset robert (41165) 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 - V193 (numeric)],
1: [1 - V250 (numeric)],
2: [2 - V432 (numeric)],
3: [3 - V568 (numeric)],
4: [4 - V575 (numeric)],
5: [5 - V645 (numeric)],
6: [6 - V828 (numeric)],
7: [7 - V920 (numeric)],
8: [8 - V954 (numeric)],
9: [9 - V994 (numeric)],
10: [10 - V1181 (numeric)],
11: [11 - V1228 (numeric)],
1... | {'MajorityClassSize': 208.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 192.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | robert_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V193",
"V250",
"V432",
"V568",
"V575",
"V645",
"V828",
"V920",
"V954",
"V994",
"V1181",
"V1228",
"V1242",
"V1269",
"V1291",
"V1372",
"V1438",
"V1485",
"V1558",
"V1564",
"V1593",
"V1603",
"V1921",
"V2014",
"V2171",
"V2409",
"V2505",
"V2602",
"V2606",
"V2637"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,529 |
362,984 | predictive_accuracy | accuracy_score | robert_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset robert (41165) 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 - V142 (numeric)],
1: [1 - V197 (numeric)],
2: [2 - V248 (numeric)],
3: [3 - V285 (numeric)],
4: [4 - V390 (numeric)],
5: [5 - V444 (numeric)],
6: [6 - V448 (numeric)],
7: [7 - V611 (numeric)],
8: [8 - V659 (numeric)],
9: [9 - V831 (numeric)],
10: [10 - V836 (numeric)],
11: [11 - V884 (numeric)],
12:... | {'MajorityClassSize': 209.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 191.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | robert_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V142",
"V197",
"V248",
"V285",
"V390",
"V444",
"V448",
"V611",
"V659",
"V831",
"V836",
"V884",
"V888",
"V957",
"V1025",
"V1065",
"V1150",
"V1453",
"V1544",
"V1772",
"V1829",
"V1870",
"V1873",
"V1944",
"V1982",
"V2004",
"V2097",
"V2099",
"V2162",
"V2218",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,530 |
361,857 | predictive_accuracy | accuracy_score | timing-attack-dataset-15-micro-seconds-delay-2022-09-18 | Bleichenbacher Timing Attack: 15 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': 959.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 866.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9995.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-15-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... | 3,531 |
362,979 | predictive_accuracy | accuracy_score | volkert_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset volkert (41166) 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 - V3 (numeric)],
1: [1 - V7 (numeric)],
2: [2 - V8 (numeric)],
3: [3 - V9 (numeric)],
4: [4 - V10 (numeric)],
5: [5 - V13 (numeric)],
6: [6 - V14 (numeric)],
7: [7 - V15 (numeric)],
8: [8 - V16 (numeric)],
9: [9 - V17 (numeric)],
10: [10 - V23 (numeric)],
11: [11 - V25 (numeric)],
12: [12 - V26 (nume... | {'MajorityClassSize': 439.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 47.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0... | volkert_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V3",
"V7",
"V8",
"V9",
"V10",
"V13",
"V14",
"V15",
"V16",
"V17",
"V23",
"V25",
"V26",
"V27",
"V29",
"V32",
"V34",
"V35",
"V36",
"V38",
"V39",
"V40",
"V42",
"V44",
"V45",
"V47",
"V49",
"V51",
"V54",
"V55",
"V56",
"V59",
"V60",
"V62",
"V63",
"V66"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,532 |
363,151 | predictive_accuracy | accuracy_score | Stylized_Meta_Album_APL_Mini | Airplanes dataset with different aiplane models | {0: [0 - FILE_NAME (string)], 1: [1 - CATEGORY (string)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Stylized_Meta_Album_APL_Mini | [
"FILE_NAME"
] | [
false
] | 3,533 |
362,986 | predictive_accuracy | accuracy_score | robert_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset robert (41165) 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 - V11 (numeric)],
1: [1 - V34 (numeric)],
2: [2 - V218 (numeric)],
3: [3 - V233 (numeric)],
4: [4 - V281 (numeric)],
5: [5 - V309 (numeric)],
6: [6 - V550 (numeric)],
7: [7 - V609 (numeric)],
8: [8 - V651 (numeric)],
9: [9 - V670 (numeric)],
10: [10 - V737 (numeric)],
11: [11 - V810 (numeric)],
12: [... | {'MajorityClassSize': 208.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 192.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | robert_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V11",
"V34",
"V218",
"V233",
"V281",
"V309",
"V550",
"V609",
"V651",
"V670",
"V737",
"V810",
"V996",
"V1137",
"V1233",
"V1275",
"V1289",
"V1362",
"V1486",
"V1571",
"V1618",
"V1683",
"V1751",
"V1779",
"V1817",
"V1822",
"V1884",
"V2028",
"V2091",
"V2100",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,534 |
362,980 | predictive_accuracy | accuracy_score | volkert_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset volkert (41166) 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 - V5 (numeric)],
1: [1 - V6 (numeric)],
2: [2 - V7 (numeric)],
3: [3 - V9 (numeric)],
4: [4 - V10 (numeric)],
5: [5 - V13 (numeric)],
6: [6 - V15 (numeric)],
7: [7 - V16 (numeric)],
8: [8 - V17 (numeric)],
9: [9 - V18 (numeric)],
10: [10 - V20 (numeric)],
11: [11 - V22 (numeric)],
12: [12 - V23 (nume... | {'MajorityClassSize': 439.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 47.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0... | volkert_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V5",
"V6",
"V7",
"V9",
"V10",
"V13",
"V15",
"V16",
"V17",
"V18",
"V20",
"V22",
"V23",
"V25",
"V26",
"V27",
"V29",
"V30",
"V31",
"V34",
"V36",
"V39",
"V40",
"V41",
"V42",
"V43",
"V45",
"V46",
"V47",
"V55",
"V58",
"V59",
"V62",
"V64",
"V65",
"V66"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,535 |
361,841 | predictive_accuracy | accuracy_score | timing-attack-dataset-10-micro-seconds-delay-2022-09-04 | Bleichenbacher Timing Attack: 10 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': 1004.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 867.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9995.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0... | timing-attack-dataset-10-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... | 3,536 |
361,891 | predictive_accuracy | accuracy_score | timing-attack-dataset-35-micro-seconds-delay-2022-09-04 | Bleichenbacher Timing Attack: 35 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': 978.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 858.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9998.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-35-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... | 3,537 |
361,881 | predictive_accuracy | accuracy_score | timing-attack-dataset-30-micro-seconds-delay-2022-09-04 | Bleichenbacher Timing Attack: 30 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': 955.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 861.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9996.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-30-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... | 3,539 |
362,996 | predictive_accuracy | accuracy_score | guillermo_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset guillermo (41159) 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 - V166 (numeric)],
1: [1 - V169 (numeric)],
2: [2 - V233 (numeric)],
3: [3 - V245 (numeric)],
4: [4 - V328 (numeric)],
5: [5 - V387 (numeric)],
6: [6 - V415 (numeric)],
7: [7 - V433 (numeric)],
8: [8 - V445 (numeric)],
9: [9 - V457 (numeric)],
10: [10 - V459 (numeric)],
11: [11 - V634 (numeric)],
12:... | {'MajorityClassSize': 1200.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 800.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | guillermo_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V166",
"V169",
"V233",
"V245",
"V328",
"V387",
"V415",
"V433",
"V445",
"V457",
"V459",
"V634",
"V792",
"V794",
"V849",
"V860",
"V911",
"V929",
"V951",
"V1098",
"V1099",
"V1111",
"V1159",
"V1254",
"V1290",
"V1351",
"V1409",
"V1411",
"V1454",
"V1467",
"V161... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,540 |
362,993 | predictive_accuracy | accuracy_score | guillermo_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset guillermo (41159) 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 - V12 (numeric)],
1: [1 - V23 (numeric)],
2: [2 - V36 (numeric)],
3: [3 - V70 (numeric)],
4: [4 - V94 (numeric)],
5: [5 - V121 (numeric)],
6: [6 - V142 (numeric)],
7: [7 - V173 (numeric)],
8: [8 - V209 (numeric)],
9: [9 - V312 (numeric)],
10: [10 - V317 (numeric)],
11: [11 - V341 (numeric)],
12: [12 ... | {'MajorityClassSize': 1200.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 800.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | guillermo_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V12",
"V23",
"V36",
"V70",
"V94",
"V121",
"V142",
"V173",
"V209",
"V312",
"V317",
"V341",
"V361",
"V378",
"V527",
"V576",
"V737",
"V743",
"V975",
"V1090",
"V1093",
"V1132",
"V1134",
"V1170",
"V1269",
"V1294",
"V1325",
"V1378",
"V1407",
"V1448",
"V1530",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,541 |
361,839 | predictive_accuracy | accuracy_score | timing-attack-dataset-256-micro-seconds-delay-2022-09-12 | Bleichenbacher Timing Attack: 256 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': 959.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 878.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9995.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-256-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... | 3,542 |
361,888 | predictive_accuracy | accuracy_score | timing-attack-dataset-30-micro-seconds-delay-2022-09-19 | Bleichenbacher Timing Attack: 30 micro seconds dataset created on 2022-09-19
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': 954.0,
'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-30-micro-seconds-delay-2022-09-19 | [
"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... | 3,543 |
363,158 | predictive_accuracy | accuracy_score | Stylized_Meta_Album_BRD_Mini | Birds dataset for image classification | {0: [0 - FILE_NAME (string)], 1: [1 - CATEGORY (string)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Stylized_Meta_Album_BRD_Mini | [
"FILE_NAME"
] | [
false
] | 3,544 |
361,900 | predictive_accuracy | accuracy_score | timing-attack-dataset-35-micro-seconds-delay-2022-09-21 | Bleichenbacher Timing Attack: 35 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': 938.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 879.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9990.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-35-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... | 3,545 |
362,998 | predictive_accuracy | accuracy_score | riccardo_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset riccardo (41161) 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 - V12 (numeric)],
1: [1 - V23 (numeric)],
2: [2 - V36 (numeric)],
3: [3 - V70 (numeric)],
4: [4 - V94 (numeric)],
5: [5 - V121 (numeric)],
6: [6 - V142 (numeric)],
7: [7 - V173 (numeric)],
8: [8 - V209 (numeric)],
9: [9 - V312 (numeric)],
10: [10 - V317 (numeric)],
11: [11 - V341 (numeric)],
12: [12 ... | {'MajorityClassSize': 1500.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 500.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | riccardo_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V12",
"V23",
"V36",
"V70",
"V94",
"V121",
"V142",
"V173",
"V209",
"V312",
"V317",
"V341",
"V361",
"V378",
"V527",
"V576",
"V737",
"V743",
"V975",
"V1090",
"V1093",
"V1132",
"V1134",
"V1170",
"V1269",
"V1294",
"V1325",
"V1378",
"V1407",
"V1448",
"V1530",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,546 |
361,887 | predictive_accuracy | accuracy_score | timing-attack-dataset-30-micro-seconds-delay-2022-09-18 | Bleichenbacher Timing Attack: 30 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': 969.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 841.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9999.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-30-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... | 3,547 |
363,231 | predictive_accuracy | accuracy_score | Flare | **flare** dataset from the **KEEL** repository: https://sci2s.ugr.es/keel/category.php?cat=clas | {0: [0 - LargestSpotSize (nominal)],
1: [1 - SpotDistribution (nominal)],
2: [2 - Activity (nominal)],
3: [3 - Evolution (nominal)],
4: [4 - Prev24Hour (nominal)],
5: [5 - HistComplex (nominal)],
6: [6 - BecomeHist (nominal)],
7: [7 - Area (nominal)],
8: [8 - C-class (nominal)],
9: [9 - M-class (nominal)],
10... | {'MajorityClassSize': 331.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 43.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 1066.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 12.0,
... | Flare | [
"LargestSpotSize",
"SpotDistribution",
"Activity",
"Evolution",
"Prev24Hour",
"HistComplex",
"BecomeHist",
"Area",
"C-class",
"M-class",
"X-class"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,548 |
363,239 | predictive_accuracy | accuracy_score | Flare | **flare** dataset from the **KEEL** repository: https://sci2s.ugr.es/keel/category.php?cat=clas | {0: [0 - LargestSpotSize (nominal)],
1: [1 - SpotDistribution (nominal)],
2: [2 - Activity (nominal)],
3: [3 - Evolution (nominal)],
4: [4 - Prev24Hour (nominal)],
5: [5 - HistComplex (nominal)],
6: [6 - BecomeHist (nominal)],
7: [7 - Area (nominal)],
8: [8 - C-class (nominal)],
9: [9 - M-class (nominal)],
10... | {'MajorityClassSize': 331.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 43.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 1066.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 12.0,
... | Flare | [
"LargestSpotSize",
"SpotDistribution",
"Activity",
"Evolution",
"Prev24Hour",
"HistComplex",
"BecomeHist",
"Area",
"C-class",
"M-class",
"X-class"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,549 |
361,870 | predictive_accuracy | accuracy_score | timing-attack-dataset-20-micro-seconds-delay-2022-09-21 | Bleichenbacher Timing Attack: 20 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': 977.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 826.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9999.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-20-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... | 3,550 |
363,230 | predictive_accuracy | accuracy_score | Flare | **flare** dataset from the **KEEL** repository: https://sci2s.ugr.es/keel/category.php?cat=clas | {0: [0 - LargestSpotSize (nominal)],
1: [1 - SpotDistribution (nominal)],
2: [2 - Activity (nominal)],
3: [3 - Evolution (nominal)],
4: [4 - Prev24Hour (nominal)],
5: [5 - HistComplex (nominal)],
6: [6 - BecomeHist (nominal)],
7: [7 - Area (nominal)],
8: [8 - C-class (nominal)],
9: [9 - M-class (nominal)],
10... | {'MajorityClassSize': 331.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 43.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 1066.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 12.0,
... | Flare | [
"LargestSpotSize",
"SpotDistribution",
"Activity",
"Evolution",
"Prev24Hour",
"HistComplex",
"BecomeHist",
"Area",
"C-class",
"M-class",
"X-class"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,551 |
362,997 | predictive_accuracy | accuracy_score | guillermo_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset guillermo (41159) 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 - V7 (numeric)],
1: [1 - V21 (numeric)],
2: [2 - V130 (numeric)],
3: [3 - V138 (numeric)],
4: [4 - V166 (numeric)],
5: [5 - V183 (numeric)],
6: [6 - V327 (numeric)],
7: [7 - V360 (numeric)],
8: [8 - V388 (numeric)],
9: [9 - V396 (numeric)],
10: [10 - V440 (numeric)],
11: [11 - V480 (numeric)],
12: [1... | {'MajorityClassSize': 1200.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 800.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | guillermo_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V7",
"V21",
"V130",
"V138",
"V166",
"V183",
"V327",
"V360",
"V388",
"V396",
"V440",
"V480",
"V591",
"V673",
"V731",
"V754",
"V762",
"V810",
"V884",
"V936",
"V965",
"V995",
"V1040",
"V1059",
"V1081",
"V1083",
"V1116",
"V1203",
"V1240",
"V1250",
"V1266",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,552 |
363,238 | predictive_accuracy | accuracy_score | Flare | **flare** dataset from the **KEEL** repository: https://sci2s.ugr.es/keel/category.php?cat=clas | {0: [0 - LargestSpotSize (nominal)],
1: [1 - SpotDistribution (nominal)],
2: [2 - Activity (nominal)],
3: [3 - Evolution (nominal)],
4: [4 - Prev24Hour (nominal)],
5: [5 - HistComplex (nominal)],
6: [6 - BecomeHist (nominal)],
7: [7 - Area (nominal)],
8: [8 - C-class (nominal)],
9: [9 - M-class (nominal)],
10... | {'MajorityClassSize': 331.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 43.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 1066.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 12.0,
... | Flare | [
"LargestSpotSize",
"SpotDistribution",
"Activity",
"Evolution",
"Prev24Hour",
"HistComplex",
"BecomeHist",
"Area",
"C-class",
"M-class",
"X-class"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,553 |
363,232 | predictive_accuracy | accuracy_score | Flare | **flare** dataset from the **KEEL** repository: https://sci2s.ugr.es/keel/category.php?cat=clas | {0: [0 - LargestSpotSize (nominal)],
1: [1 - SpotDistribution (nominal)],
2: [2 - Activity (nominal)],
3: [3 - Evolution (nominal)],
4: [4 - Prev24Hour (nominal)],
5: [5 - HistComplex (nominal)],
6: [6 - BecomeHist (nominal)],
7: [7 - Area (nominal)],
8: [8 - C-class (nominal)],
9: [9 - M-class (nominal)],
10... | {'MajorityClassSize': 331.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 43.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 1066.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 12.0,
... | Flare | [
"LargestSpotSize",
"SpotDistribution",
"Activity",
"Evolution",
"Prev24Hour",
"HistComplex",
"BecomeHist",
"Area",
"C-class",
"M-class",
"X-class"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,554 |
363,237 | predictive_accuracy | accuracy_score | Flare | **flare** dataset from the **KEEL** repository: https://sci2s.ugr.es/keel/category.php?cat=clas | {0: [0 - LargestSpotSize (nominal)],
1: [1 - SpotDistribution (nominal)],
2: [2 - Activity (nominal)],
3: [3 - Evolution (nominal)],
4: [4 - Prev24Hour (nominal)],
5: [5 - HistComplex (nominal)],
6: [6 - BecomeHist (nominal)],
7: [7 - Area (nominal)],
8: [8 - C-class (nominal)],
9: [9 - M-class (nominal)],
10... | {'MajorityClassSize': 331.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 43.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 1066.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 12.0,
... | Flare | [
"LargestSpotSize",
"SpotDistribution",
"Activity",
"Evolution",
"Prev24Hour",
"HistComplex",
"BecomeHist",
"Area",
"C-class",
"M-class",
"X-class"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,555 |
363,233 | predictive_accuracy | accuracy_score | Flare | **flare** dataset from the **KEEL** repository: https://sci2s.ugr.es/keel/category.php?cat=clas | {0: [0 - LargestSpotSize (nominal)],
1: [1 - SpotDistribution (nominal)],
2: [2 - Activity (nominal)],
3: [3 - Evolution (nominal)],
4: [4 - Prev24Hour (nominal)],
5: [5 - HistComplex (nominal)],
6: [6 - BecomeHist (nominal)],
7: [7 - Area (nominal)],
8: [8 - C-class (nominal)],
9: [9 - M-class (nominal)],
10... | {'MajorityClassSize': 331.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 43.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 1066.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 12.0,
... | Flare | [
"LargestSpotSize",
"SpotDistribution",
"Activity",
"Evolution",
"Prev24Hour",
"HistComplex",
"BecomeHist",
"Area",
"C-class",
"M-class",
"X-class"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,556 |
363,169 | predictive_accuracy | accuracy_score | Stylized_Meta_Album_INS2_Mini | Insects dataset for Insect Pest Recognition | {0: [0 - FILE_NAME (string)], 1: [1 - CATEGORY (string)]} | {'MajorityClassSize': 40.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 40.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 2.0,
'NumberOfInstances': 800.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
'co... | Stylized_Meta_Album_INS2_Mini | [
"FILE_NAME"
] | [
false
] | 3,557 |
363,527 | predictive_accuracy | accuracy_score | product_sentiment_machine_hack | Classify the sentiment (4-way classification) of user reviews of products based on the review
text and product type (e.g. Tablet, Mobile, etc.). Intuitively, we expect most of the predictive signal to
lie in the text, but predictions can be further improved by accounting for the fact that certain types of
p... | {0: [0 - Unnamed: 0 (numeric)],
1: [1 - Text_ID (numeric)],
2: [2 - Product_Description (string)],
3: [3 - Product_Type (numeric)],
4: [4 - Sentiment (nominal)]} | {'MajorityClassSize': 3017.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 97.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 5091.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 1.0,
'... | product_sentiment_machine_hack | [
"Unnamed: 0",
"Text_ID",
"Product_Description",
"Product_Type"
] | [
false,
false,
false,
false
] | 3,558 |
361,847 | predictive_accuracy | accuracy_score | timing-attack-dataset-10-micro-seconds-delay-2022-09-18 | Bleichenbacher Timing Attack: 10 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': 978.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 855.0,
'NumberOfClasses': 11.0,
'NumberOfFeatures': 125.0,
'NumberOfInstances': 9998.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 124.0,
'NumberOfSymbolicFeatures': 0.... | timing-attack-dataset-10-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... | 3,559 |
363,146 | predictive_accuracy | accuracy_score | BraidFlow | Entering a cognitive state of flow is a natural response of the mind that allows people to fully concentrate and cope with tedious, and often repetitive tasks. Understanding how to trigger or sustain flow remains limited by retrospective surveys, presenting a need to better document flow. This dataset is used to study ... | {0: [0 - UID (numeric)],
1: [1 - task_id (numeric)],
2: [2 - action_absorption (numeric)],
3: [3 - action_accord (numeric)],
4: [4 - action_fluidity (numeric)],
5: [5 - care (numeric)],
6: [6 - challenge_match (numeric)],
7: [7 - clear_mindedness (numeric)],
8: [8 - control (numeric)],
9: [9 - demand (numeric)... | {'MajorityClassSize': 37.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 17.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 124.0,
'NumberOfInstances': 72.0,
'NumberOfInstancesWithMissingValues': 21.0,
'NumberOfMissingValues': 159.0,
'NumberOfNumericFeatures': 117.0,
'NumberOfSymbolicFeatures': 6.0,... | BraidFlow | [
"UID",
"task_id",
"action_absorption",
"action_accord",
"action_fluidity",
"care",
"challenge_match",
"clear_mindedness",
"control",
"demand",
"factor_absorption_by_activity",
"factor_fluency_of_performance",
"factor_perceived_fit_of_skill_and_task_demands",
"factor_subjective_value_of_act... | [
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,
false,
false,
false,
false,
false,
fa... | 3,560 |
363,012 | predictive_accuracy | accuracy_score | Click_prediction_small_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Click_prediction_small (42733) 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,
ncl... | {0: [0 - impression (numeric)],
1: [1 - url_hash (numeric)],
2: [2 - ad_id (nominal)],
3: [3 - advertiser_id (nominal)],
4: [4 - depth (numeric)],
5: [5 - position (numeric)],
6: [6 - query_id (numeric)],
7: [7 - keyword_id (nominal)],
8: [8 - title_id (nominal)],
9: [9 - description_id (nominal)],
10: [10 - ... | {'MajorityClassSize': 1663.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 337.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 7.0,
... | Click_prediction_small_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"impression",
"url_hash",
"ad_id",
"advertiser_id",
"depth",
"position",
"query_id",
"keyword_id",
"title_id",
"description_id",
"user_id"
] | [
false,
false,
true,
true,
false,
false,
false,
true,
true,
true,
true
] | 3,561 |
363,011 | predictive_accuracy | accuracy_score | Click_prediction_small_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Click_prediction_small (42733) 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,
ncl... | {0: [0 - impression (numeric)],
1: [1 - url_hash (numeric)],
2: [2 - ad_id (nominal)],
3: [3 - advertiser_id (nominal)],
4: [4 - depth (numeric)],
5: [5 - position (numeric)],
6: [6 - query_id (numeric)],
7: [7 - keyword_id (nominal)],
8: [8 - title_id (nominal)],
9: [9 - description_id (nominal)],
10: [10 - ... | {'MajorityClassSize': 1663.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 337.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 5.0,
'NumberOfSymbolicFeatures': 7.0,
... | Click_prediction_small_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"impression",
"url_hash",
"ad_id",
"advertiser_id",
"depth",
"position",
"query_id",
"keyword_id",
"title_id",
"description_id",
"user_id"
] | [
false,
false,
true,
true,
false,
false,
false,
true,
true,
true,
true
] | 3,562 |
362,999 | predictive_accuracy | accuracy_score | riccardo_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset riccardo (41161) 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 - V166 (numeric)],
1: [1 - V169 (numeric)],
2: [2 - V233 (numeric)],
3: [3 - V245 (numeric)],
4: [4 - V328 (numeric)],
5: [5 - V387 (numeric)],
6: [6 - V415 (numeric)],
7: [7 - V433 (numeric)],
8: [8 - V445 (numeric)],
9: [9 - V457 (numeric)],
10: [10 - V459 (numeric)],
11: [11 - V634 (numeric)],
12:... | {'MajorityClassSize': 1500.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 500.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | riccardo_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V166",
"V169",
"V233",
"V245",
"V328",
"V387",
"V415",
"V433",
"V445",
"V457",
"V459",
"V634",
"V792",
"V794",
"V849",
"V860",
"V911",
"V929",
"V951",
"V1098",
"V1099",
"V1111",
"V1159",
"V1254",
"V1290",
"V1351",
"V1409",
"V1411",
"V1454",
"V1467",
"V161... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,564 |
363,234 | predictive_accuracy | accuracy_score | Flare | **flare** dataset from the **KEEL** repository: https://sci2s.ugr.es/keel/category.php?cat=clas | {0: [0 - LargestSpotSize (nominal)],
1: [1 - SpotDistribution (nominal)],
2: [2 - Activity (nominal)],
3: [3 - Evolution (nominal)],
4: [4 - Prev24Hour (nominal)],
5: [5 - HistComplex (nominal)],
6: [6 - BecomeHist (nominal)],
7: [7 - Area (nominal)],
8: [8 - C-class (nominal)],
9: [9 - M-class (nominal)],
10... | {'MajorityClassSize': 331.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 43.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 1066.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 12.0,
... | Flare | [
"LargestSpotSize",
"SpotDistribution",
"Activity",
"Evolution",
"Prev24Hour",
"HistComplex",
"BecomeHist",
"Area",
"C-class",
"M-class",
"X-class"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,565 |
363,236 | predictive_accuracy | accuracy_score | Flare | **flare** dataset from the **KEEL** repository: https://sci2s.ugr.es/keel/category.php?cat=clas | {0: [0 - LargestSpotSize (nominal)],
1: [1 - SpotDistribution (nominal)],
2: [2 - Activity (nominal)],
3: [3 - Evolution (nominal)],
4: [4 - Prev24Hour (nominal)],
5: [5 - HistComplex (nominal)],
6: [6 - BecomeHist (nominal)],
7: [7 - Area (nominal)],
8: [8 - C-class (nominal)],
9: [9 - M-class (nominal)],
10... | {'MajorityClassSize': 331.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 43.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 1066.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 12.0,
... | Flare | [
"LargestSpotSize",
"SpotDistribution",
"Activity",
"Evolution",
"Prev24Hour",
"HistComplex",
"BecomeHist",
"Area",
"C-class",
"M-class",
"X-class"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,566 |
363,240 | predictive_accuracy | accuracy_score | Flare | **flare** dataset from the **KEEL** repository: https://sci2s.ugr.es/keel/category.php?cat=clas | {0: [0 - LargestSpotSize (nominal)],
1: [1 - SpotDistribution (nominal)],
2: [2 - Activity (nominal)],
3: [3 - Evolution (nominal)],
4: [4 - Prev24Hour (nominal)],
5: [5 - HistComplex (nominal)],
6: [6 - BecomeHist (nominal)],
7: [7 - Area (nominal)],
8: [8 - C-class (nominal)],
9: [9 - M-class (nominal)],
10... | {'MajorityClassSize': 331.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 43.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 1066.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 12.0,
... | Flare | [
"LargestSpotSize",
"SpotDistribution",
"Activity",
"Evolution",
"Prev24Hour",
"HistComplex",
"BecomeHist",
"Area",
"C-class",
"M-class",
"X-class"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,567 |
363,235 | predictive_accuracy | accuracy_score | Flare | **flare** dataset from the **KEEL** repository: https://sci2s.ugr.es/keel/category.php?cat=clas | {0: [0 - LargestSpotSize (nominal)],
1: [1 - SpotDistribution (nominal)],
2: [2 - Activity (nominal)],
3: [3 - Evolution (nominal)],
4: [4 - Prev24Hour (nominal)],
5: [5 - HistComplex (nominal)],
6: [6 - BecomeHist (nominal)],
7: [7 - Area (nominal)],
8: [8 - C-class (nominal)],
9: [9 - M-class (nominal)],
10... | {'MajorityClassSize': 331.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 43.0,
'NumberOfClasses': 6.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 1066.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 12.0,
... | Flare | [
"LargestSpotSize",
"SpotDistribution",
"Activity",
"Evolution",
"Prev24Hour",
"HistComplex",
"BecomeHist",
"Area",
"C-class",
"M-class",
"X-class"
] | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,568 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.