uid int64 2 364k | orig_metric stringclasses 30
values | sklearn_metric stringclasses 9
values | dataset_name stringlengths 2 124 | dataset_description stringlengths 3 13k ⌀ | dataset_features stringlengths 41 3.57M | task_description stringlengths 627 762 | task_name stringlengths 2 124 | attribute_names listlengths 0 100k | categorical_indicator listlengths 0 100k | __index_level_0__ int64 0 3.8k |
|---|---|---|---|---|---|---|---|---|---|---|
362,781 | predictive_accuracy | accuracy_score | dionis_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset dionis (41167) with
seed=2
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int =... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 12.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 4.0,
'NumberOfClasses': 355.0,
'NumberOfFeatures': 61.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 60.0,
'NumberOfSymbolicFeatures': 1.0,
... | dionis_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27",
"V28",
"V29",
"V30",
"V31",
"V32",
"V33",
"V34",
"V35",
"V36",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 3,255 |
362,866 | predictive_accuracy | accuracy_score | car_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset car (40975) 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 = 10... | {0: [0 - buying (nominal)],
1: [1 - maint (nominal)],
2: [2 - doors (nominal)],
3: [3 - persons (nominal)],
4: [4 - lug_boot (nominal)],
5: [5 - safety (nominal)],
6: [6 - class (nominal)]} | {'MajorityClassSize': 1210.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 65.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 1728.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 7.0,
'... | car_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"buying",
"maint",
"doors",
"persons",
"lug_boot",
"safety"
] | [
true,
true,
true,
true,
true,
true
] | 3,256 |
362,827 | predictive_accuracy | accuracy_score | micro-mass_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset micro-mass (1515) 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 - V34 (numeric)],
1: [1 - V45 (numeric)],
2: [2 - V54 (numeric)],
3: [3 - V73 (numeric)],
4: [4 - V92 (numeric)],
5: [5 - V99 (numeric)],
6: [6 - V111 (numeric)],
7: [7 - V126 (numeric)],
8: [8 - V145 (numeric)],
9: [9 - V163 (numeric)],
10: [10 - V164 (numeric)],
11: [11 - V169 (numeric)],
12: [12 -... | {'MajorityClassSize': 60.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 11.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 571.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0,
... | micro-mass_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V34",
"V45",
"V54",
"V73",
"V92",
"V99",
"V111",
"V126",
"V145",
"V163",
"V164",
"V169",
"V199",
"V205",
"V217",
"V219",
"V227",
"V242",
"V249",
"V255",
"V273",
"V278",
"V282",
"V335",
"V356",
"V371",
"V380",
"V394",
"V423",
"V433",
"V448",
"V449",
"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... | 3,257 |
362,767 | predictive_accuracy | accuracy_score | fabert_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset fabert (41164) 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 - V21 (numeric)],
1: [1 - V28 (numeric)],
2: [2 - V44 (numeric)],
3: [3 - V58 (numeric)],
4: [4 - V61 (numeric)],
5: [5 - V68 (numeric)],
6: [6 - V90 (numeric)],
7: [7 - V100 (numeric)],
8: [8 - V102 (numeric)],
9: [9 - V125 (numeric)],
10: [10 - V126 (numeric)],
11: [11 - V132 (numeric)],
12: [12 - ... | {'MajorityClassSize': 468.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 122.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0... | fabert_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V21",
"V28",
"V44",
"V58",
"V61",
"V68",
"V90",
"V100",
"V102",
"V125",
"V126",
"V132",
"V136",
"V141",
"V150",
"V153",
"V155",
"V158",
"V170",
"V171",
"V176",
"V202",
"V207",
"V226",
"V244",
"V266",
"V269",
"V271",
"V275",
"V286",
"V287",
"V304",
"V3... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,258 |
362,725 | predictive_accuracy | accuracy_score | KDDCup09_appetency_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset KDDCup09_appetency (1111) 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... | {0: [0 - Var1 (numeric)],
1: [1 - Var2 (numeric)],
2: [2 - Var5 (numeric)],
3: [3 - Var6 (numeric)],
4: [4 - Var12 (numeric)],
5: [5 - Var14 (numeric)],
6: [6 - Var18 (numeric)],
7: [7 - Var24 (numeric)],
8: [8 - Var25 (numeric)],
9: [9 - Var28 (numeric)],
10: [10 - Var36 (numeric)],
11: [11 - Var45 (numeric... | {'MajorityClassSize': 1964.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 36.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 93.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 2000.0,
'NumberOfMissingValues': 125883.0,
'NumberOfNumericFeatures': 73.0,
'NumberOfSymbolicFeatures... | KDDCup09_appetency_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Var1",
"Var2",
"Var5",
"Var6",
"Var12",
"Var14",
"Var18",
"Var24",
"Var25",
"Var28",
"Var36",
"Var45",
"Var46",
"Var47",
"Var49",
"Var50",
"Var51",
"Var53",
"Var56",
"Var60",
"Var61",
"Var62",
"Var66",
"Var68",
"Var69",
"Var72",
"Var73",
"Var80",
"Var81",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,259 |
362,864 | predictive_accuracy | accuracy_score | car_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset car (40975) 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 = 10... | {0: [0 - buying (nominal)],
1: [1 - maint (nominal)],
2: [2 - doors (nominal)],
3: [3 - persons (nominal)],
4: [4 - lug_boot (nominal)],
5: [5 - safety (nominal)],
6: [6 - class (nominal)]} | {'MajorityClassSize': 1210.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 65.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 1728.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 7.0,
'... | car_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"buying",
"maint",
"doors",
"persons",
"lug_boot",
"safety"
] | [
true,
true,
true,
true,
true,
true
] | 3,260 |
362,825 | predictive_accuracy | accuracy_score | micro-mass_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset micro-mass (1515) 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 - V49 (numeric)],
1: [1 - V50 (numeric)],
2: [2 - V73 (numeric)],
3: [3 - V97 (numeric)],
4: [4 - V123 (numeric)],
5: [5 - V129 (numeric)],
6: [6 - V130 (numeric)],
7: [7 - V133 (numeric)],
8: [8 - V138 (numeric)],
9: [9 - V223 (numeric)],
10: [10 - V228 (numeric)],
11: [11 - V236 (numeric)],
12: [12... | {'MajorityClassSize': 60.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 11.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 571.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0,
... | micro-mass_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V49",
"V50",
"V73",
"V97",
"V123",
"V129",
"V130",
"V133",
"V138",
"V223",
"V228",
"V236",
"V250",
"V255",
"V266",
"V268",
"V276",
"V330",
"V343",
"V391",
"V406",
"V413",
"V422",
"V430",
"V462",
"V470",
"V476",
"V496",
"V508",
"V510",
"V521",
"V527",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,261 |
362,853 | predictive_accuracy | accuracy_score | pc4_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset pc4 (1049) 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 = 10,... | {0: [0 - LOC_BLANK (numeric)],
1: [1 - BRANCH_COUNT (numeric)],
2: [2 - CALL_PAIRS (numeric)],
3: [3 - LOC_CODE_AND_COMMENT (numeric)],
4: [4 - LOC_COMMENTS (numeric)],
5: [5 - CONDITION_COUNT (numeric)],
6: [6 - CYCLOMATIC_COMPLEXITY (numeric)],
7: [7 - CYCLOMATIC_DENSITY (numeric)],
8: [8 - DECISION_COUNT (nu... | {'MajorityClassSize': 1280.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 178.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 38.0,
'NumberOfInstances': 1458.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 37.0,
'NumberOfSymbolicFeatures': 1.0,... | pc4_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"LOC_BLANK",
"BRANCH_COUNT",
"CALL_PAIRS",
"LOC_CODE_AND_COMMENT",
"LOC_COMMENTS",
"CONDITION_COUNT",
"CYCLOMATIC_COMPLEXITY",
"CYCLOMATIC_DENSITY",
"DECISION_COUNT",
"DECISION_DENSITY",
"DESIGN_COMPLEXITY",
"DESIGN_DENSITY",
"EDGE_COUNT",
"ESSENTIAL_COMPLEXITY",
"ESSENTIAL_DENSITY",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,262 |
362,761 | predictive_accuracy | accuracy_score | fabert_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset fabert (41164) 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 - V30 (numeric)],
1: [1 - V31 (numeric)],
2: [2 - V40 (numeric)],
3: [3 - V46 (numeric)],
4: [4 - V61 (numeric)],
5: [5 - V66 (numeric)],
6: [6 - V77 (numeric)],
7: [7 - V80 (numeric)],
8: [8 - V81 (numeric)],
9: [9 - V82 (numeric)],
10: [10 - V109 (numeric)],
11: [11 - V135 (numeric)],
12: [12 - V13... | {'MajorityClassSize': 468.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 122.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0... | fabert_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V30",
"V31",
"V40",
"V46",
"V61",
"V66",
"V77",
"V80",
"V81",
"V82",
"V109",
"V135",
"V139",
"V145",
"V154",
"V161",
"V166",
"V169",
"V173",
"V184",
"V189",
"V198",
"V207",
"V211",
"V221",
"V238",
"V239",
"V247",
"V257",
"V269",
"V281",
"V290",
"V292"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,263 |
362,762 | predictive_accuracy | accuracy_score | fabert_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset fabert (41164) 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 - V2 (numeric)],
1: [1 - V4 (numeric)],
2: [2 - V24 (numeric)],
3: [3 - V28 (numeric)],
4: [4 - V33 (numeric)],
5: [5 - V58 (numeric)],
6: [6 - V61 (numeric)],
7: [7 - V67 (numeric)],
8: [8 - V70 (numeric)],
9: [9 - V82 (numeric)],
10: [10 - V105 (numeric)],
11: [11 - V115 (numeric)],
12: [12 - V126 ... | {'MajorityClassSize': 468.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 122.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0... | fabert_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V2",
"V4",
"V24",
"V28",
"V33",
"V58",
"V61",
"V67",
"V70",
"V82",
"V105",
"V115",
"V126",
"V127",
"V128",
"V144",
"V160",
"V167",
"V171",
"V178",
"V185",
"V190",
"V192",
"V194",
"V209",
"V217",
"V223",
"V228",
"V231",
"V235",
"V236",
"V237",
"V259",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,264 |
362,720 | predictive_accuracy | accuracy_score | KDDCup09_appetency_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset KDDCup09_appetency (1111) 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... | {0: [0 - Var4 (numeric)],
1: [1 - Var5 (numeric)],
2: [2 - Var9 (numeric)],
3: [3 - Var11 (numeric)],
4: [4 - Var13 (numeric)],
5: [5 - Var14 (numeric)],
6: [6 - Var17 (numeric)],
7: [7 - Var21 (numeric)],
8: [8 - Var22 (numeric)],
9: [9 - Var23 (numeric)],
10: [10 - Var34 (numeric)],
11: [11 - Var35 (numeri... | {'MajorityClassSize': 1964.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 36.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 94.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 2000.0,
'NumberOfMissingValues': 118767.0,
'NumberOfNumericFeatures': 77.0,
'NumberOfSymbolicFeatures... | KDDCup09_appetency_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Var4",
"Var5",
"Var9",
"Var11",
"Var13",
"Var14",
"Var17",
"Var21",
"Var22",
"Var23",
"Var34",
"Var35",
"Var38",
"Var40",
"Var44",
"Var45",
"Var47",
"Var49",
"Var54",
"Var57",
"Var60",
"Var61",
"Var62",
"Var64",
"Var67",
"Var68",
"Var72",
"Var73",
"Var74",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,265 |
362,863 | predictive_accuracy | accuracy_score | car_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset car (40975) 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 = 10... | {0: [0 - buying (nominal)],
1: [1 - maint (nominal)],
2: [2 - doors (nominal)],
3: [3 - persons (nominal)],
4: [4 - lug_boot (nominal)],
5: [5 - safety (nominal)],
6: [6 - class (nominal)]} | {'MajorityClassSize': 1210.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 65.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 1728.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 7.0,
'... | car_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"buying",
"maint",
"doors",
"persons",
"lug_boot",
"safety"
] | [
true,
true,
true,
true,
true,
true
] | 3,266 |
362,793 | predictive_accuracy | accuracy_score | ozone-level-8hr_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset ozone-level-8hr (1487) 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_ma... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 1874.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 126.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 73.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 72.0,
'NumberOfSymbolicFeatures': 1.0,... | ozone-level-8hr_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27",
"V28",
"V29",
"V30",
"V31",
"V32",
"V33",
"V34",
"V35",
"V36",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 3,267 |
362,760 | predictive_accuracy | accuracy_score | fabert_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset fabert (41164) 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 - V2 (numeric)],
1: [1 - V4 (numeric)],
2: [2 - V7 (numeric)],
3: [3 - V12 (numeric)],
4: [4 - V17 (numeric)],
5: [5 - V22 (numeric)],
6: [6 - V25 (numeric)],
7: [7 - V29 (numeric)],
8: [8 - V39 (numeric)],
9: [9 - V54 (numeric)],
10: [10 - V57 (numeric)],
11: [11 - V60 (numeric)],
12: [12 - V66 (num... | {'MajorityClassSize': 468.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 122.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0... | fabert_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V2",
"V4",
"V7",
"V12",
"V17",
"V22",
"V25",
"V29",
"V39",
"V54",
"V57",
"V60",
"V66",
"V68",
"V93",
"V104",
"V125",
"V129",
"V181",
"V190",
"V193",
"V200",
"V210",
"V218",
"V222",
"V240",
"V253",
"V260",
"V267",
"V280",
"V286",
"V288",
"V289",
"V29... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,268 |
362,852 | predictive_accuracy | accuracy_score | cnae-9_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset cnae-9 (1468) 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 - V2 (numeric)],
1: [1 - V4 (numeric)],
2: [2 - V25 (numeric)],
3: [3 - V26 (numeric)],
4: [4 - V31 (numeric)],
5: [5 - V35 (numeric)],
6: [6 - V62 (numeric)],
7: [7 - V65 (numeric)],
8: [8 - V73 (numeric)],
9: [9 - V75 (numeric)],
10: [10 - V88 (numeric)],
11: [11 - V89 (numeric)],
12: [12 - V112 (n... | {'MajorityClassSize': 120.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 120.0,
'NumberOfClasses': 9.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 1080.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0... | cnae-9_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V2",
"V4",
"V25",
"V26",
"V31",
"V35",
"V62",
"V65",
"V73",
"V75",
"V88",
"V89",
"V112",
"V124",
"V136",
"V137",
"V139",
"V155",
"V172",
"V180",
"V183",
"V190",
"V198",
"V205",
"V206",
"V208",
"V225",
"V234",
"V240",
"V244",
"V248",
"V253",
"V254",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,269 |
362,824 | predictive_accuracy | accuracy_score | micro-mass_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset micro-mass (1515) 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 - V10 (numeric)],
1: [1 - V25 (numeric)],
2: [2 - V50 (numeric)],
3: [3 - V69 (numeric)],
4: [4 - V76 (numeric)],
5: [5 - V79 (numeric)],
6: [6 - V88 (numeric)],
7: [7 - V105 (numeric)],
8: [8 - V113 (numeric)],
9: [9 - V143 (numeric)],
10: [10 - V144 (numeric)],
11: [11 - V150 (numeric)],
12: [12 - ... | {'MajorityClassSize': 60.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 11.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 571.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0,
... | micro-mass_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V10",
"V25",
"V50",
"V69",
"V76",
"V79",
"V88",
"V105",
"V113",
"V143",
"V144",
"V150",
"V162",
"V189",
"V198",
"V246",
"V247",
"V271",
"V284",
"V319",
"V329",
"V341",
"V342",
"V356",
"V360",
"V363",
"V366",
"V398",
"V402",
"V436",
"V452",
"V460",
"V4... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,270 |
362,855 | predictive_accuracy | accuracy_score | pc4_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset pc4 (1049) 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 = 10,... | {0: [0 - LOC_BLANK (numeric)],
1: [1 - BRANCH_COUNT (numeric)],
2: [2 - CALL_PAIRS (numeric)],
3: [3 - LOC_CODE_AND_COMMENT (numeric)],
4: [4 - LOC_COMMENTS (numeric)],
5: [5 - CONDITION_COUNT (numeric)],
6: [6 - CYCLOMATIC_COMPLEXITY (numeric)],
7: [7 - CYCLOMATIC_DENSITY (numeric)],
8: [8 - DECISION_COUNT (nu... | {'MajorityClassSize': 1280.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 178.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 38.0,
'NumberOfInstances': 1458.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 37.0,
'NumberOfSymbolicFeatures': 1.0,... | pc4_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"LOC_BLANK",
"BRANCH_COUNT",
"CALL_PAIRS",
"LOC_CODE_AND_COMMENT",
"LOC_COMMENTS",
"CONDITION_COUNT",
"CYCLOMATIC_COMPLEXITY",
"CYCLOMATIC_DENSITY",
"DECISION_COUNT",
"DECISION_DENSITY",
"DESIGN_COMPLEXITY",
"DESIGN_DENSITY",
"EDGE_COUNT",
"ESSENTIAL_COMPLEXITY",
"ESSENTIAL_DENSITY",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,271 |
362,851 | predictive_accuracy | accuracy_score | cnae-9_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset cnae-9 (1468) 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 - V23 (numeric)],
1: [1 - V30 (numeric)],
2: [2 - V48 (numeric)],
3: [3 - V62 (numeric)],
4: [4 - V66 (numeric)],
5: [5 - V73 (numeric)],
6: [6 - V97 (numeric)],
7: [7 - V108 (numeric)],
8: [8 - V110 (numeric)],
9: [9 - V135 (numeric)],
10: [10 - V142 (numeric)],
11: [11 - V145 (numeric)],
12: [12 - ... | {'MajorityClassSize': 120.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 120.0,
'NumberOfClasses': 9.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 1080.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0... | cnae-9_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V23",
"V30",
"V48",
"V62",
"V66",
"V73",
"V97",
"V108",
"V110",
"V135",
"V142",
"V145",
"V151",
"V161",
"V164",
"V167",
"V171",
"V182",
"V183",
"V189",
"V218",
"V222",
"V243",
"V263",
"V285",
"V290",
"V292",
"V296",
"V307",
"V308",
"V327",
"V328",
"V3... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,272 |
362,849 | predictive_accuracy | accuracy_score | cnae-9_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset cnae-9 (1468) 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 - V16 (numeric)],
1: [1 - V22 (numeric)],
2: [2 - V27 (numeric)],
3: [3 - V33 (numeric)],
4: [4 - V47 (numeric)],
5: [5 - V50 (numeric)],
6: [6 - V53 (numeric)],
7: [7 - V67 (numeric)],
8: [8 - V75 (numeric)],
9: [9 - V94 (numeric)],
10: [10 - V96 (numeric)],
11: [11 - V98 (numeric)],
12: [12 - V106 ... | {'MajorityClassSize': 120.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 120.0,
'NumberOfClasses': 9.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 1080.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0... | cnae-9_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V16",
"V22",
"V27",
"V33",
"V47",
"V50",
"V53",
"V67",
"V75",
"V94",
"V96",
"V98",
"V106",
"V110",
"V127",
"V131",
"V162",
"V181",
"V191",
"V199",
"V209",
"V211",
"V220",
"V225",
"V238",
"V239",
"V240",
"V258",
"V263",
"V292",
"V300",
"V304",
"V308",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,273 |
362,857 | predictive_accuracy | accuracy_score | pc4_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset pc4 (1049) 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 = 10,... | {0: [0 - LOC_BLANK (numeric)],
1: [1 - BRANCH_COUNT (numeric)],
2: [2 - CALL_PAIRS (numeric)],
3: [3 - LOC_CODE_AND_COMMENT (numeric)],
4: [4 - LOC_COMMENTS (numeric)],
5: [5 - CONDITION_COUNT (numeric)],
6: [6 - CYCLOMATIC_COMPLEXITY (numeric)],
7: [7 - CYCLOMATIC_DENSITY (numeric)],
8: [8 - DECISION_COUNT (nu... | {'MajorityClassSize': 1280.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 178.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 38.0,
'NumberOfInstances': 1458.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 37.0,
'NumberOfSymbolicFeatures': 1.0,... | pc4_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"LOC_BLANK",
"BRANCH_COUNT",
"CALL_PAIRS",
"LOC_CODE_AND_COMMENT",
"LOC_COMMENTS",
"CONDITION_COUNT",
"CYCLOMATIC_COMPLEXITY",
"CYCLOMATIC_DENSITY",
"DECISION_COUNT",
"DECISION_DENSITY",
"DESIGN_COMPLEXITY",
"DESIGN_DENSITY",
"EDGE_COUNT",
"ESSENTIAL_COMPLEXITY",
"ESSENTIAL_DENSITY",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,274 |
362,843 | predictive_accuracy | accuracy_score | qsar-biodeg_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset qsar-biodeg (1494) with
seed=0
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: i... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 699.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 356.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 42.0,
'NumberOfInstances': 1055.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 41.0,
'NumberOfSymbolicFeatures': 1.0,
... | qsar-biodeg_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27",
"V28",
"V29",
"V30",
"V31",
"V32",
"V33",
"V34",
"V35",
"V36",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 3,275 |
362,848 | predictive_accuracy | accuracy_score | cnae-9_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset cnae-9 (1468) 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 - V3 (numeric)],
1: [1 - V5 (numeric)],
2: [2 - V7 (numeric)],
3: [3 - V13 (numeric)],
4: [4 - V18 (numeric)],
5: [5 - V23 (numeric)],
6: [6 - V27 (numeric)],
7: [7 - V32 (numeric)],
8: [8 - V42 (numeric)],
9: [9 - V58 (numeric)],
10: [10 - V61 (numeric)],
11: [11 - V64 (numeric)],
12: [12 - V71 (num... | {'MajorityClassSize': 120.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 120.0,
'NumberOfClasses': 9.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 1080.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0... | cnae-9_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V3",
"V5",
"V7",
"V13",
"V18",
"V23",
"V27",
"V32",
"V42",
"V58",
"V61",
"V64",
"V71",
"V72",
"V100",
"V112",
"V135",
"V139",
"V194",
"V206",
"V208",
"V214",
"V216",
"V225",
"V235",
"V238",
"V257",
"V271",
"V278",
"V286",
"V300",
"V308",
"V309",
"V3... | [
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false,
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false,
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false,
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f... | 3,278 |
362,878 | predictive_accuracy | accuracy_score | segment_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset segment (40984) 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 - short.line.density.5 (numeric)],
1: [1 - short.line.density.2 (numeric)],
2: [2 - vedge.mean (numeric)],
3: [3 - vegde.sd (numeric)],
4: [4 - hedge.mean (numeric)],
5: [5 - hedge.sd (numeric)],
6: [6 - intensity.mean (numeric)],
7: [7 - rawred.mean (numeric)],
8: [8 - rawblue.mean (numeric)],
9: [9 - ... | {'MajorityClassSize': 286.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 285.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 17.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 16.0,
'NumberOfSymbolicFeatures': 1.0,
... | segment_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"short.line.density.5",
"short.line.density.2",
"vedge.mean",
"vegde.sd",
"hedge.mean",
"hedge.sd",
"intensity.mean",
"rawred.mean",
"rawblue.mean",
"rawgreen.mean",
"exred.mean",
"exblue.mean",
"exgreen.mean",
"value.mean",
"saturation.mean",
"hue.mean"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,279 |
362,873 | predictive_accuracy | accuracy_score | kc1_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset kc1 (1067) 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 = 10,... | {0: [0 - loc (numeric)],
1: [1 - v(g) (numeric)],
2: [2 - ev(g) (numeric)],
3: [3 - iv(g) (numeric)],
4: [4 - n (numeric)],
5: [5 - v (numeric)],
6: [6 - l (numeric)],
7: [7 - d (numeric)],
8: [8 - i (numeric)],
9: [9 - e (numeric)],
10: [10 - b (numeric)],
11: [11 - t (numeric)],
12: [12 - lOCode (numeric)... | {'MajorityClassSize': 1691.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 309.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 22.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 21.0,
'NumberOfSymbolicFeatures': 1.0,... | kc1_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"loc",
"v(g)",
"ev(g)",
"iv(g)",
"n",
"v",
"l",
"d",
"i",
"e",
"b",
"t",
"lOCode",
"lOComment",
"lOBlank",
"locCodeAndComment",
"uniq_Op",
"uniq_Opnd",
"total_Op",
"total_Opnd",
"branchCount"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,280 |
362,791 | predictive_accuracy | accuracy_score | ozone-level-8hr_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset ozone-level-8hr (1487) 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_ma... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 1874.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 126.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 73.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 72.0,
'NumberOfSymbolicFeatures': 1.0,... | ozone-level-8hr_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27",
"V28",
"V29",
"V30",
"V31",
"V32",
"V33",
"V34",
"V35",
"V36",
"... | [
false,
false,
false,
false,
false,
false,
false,
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false,
false,
false,
false,
false,
false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 3,281 |
362,754 | predictive_accuracy | accuracy_score | APSFailure_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset APSFailure (41138) with
seed=1
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: i... | {0: [0 - ac_000 (numeric)],
1: [1 - af_000 (numeric)],
2: [2 - ag_001 (numeric)],
3: [3 - ag_003 (numeric)],
4: [4 - ag_004 (numeric)],
5: [5 - ag_005 (numeric)],
6: [6 - ag_006 (numeric)],
7: [7 - ag_007 (numeric)],
8: [8 - ag_009 (numeric)],
9: [9 - ak_000 (numeric)],
10: [10 - al_000 (numeric)],
11: [11 -... | {'MajorityClassSize': 1964.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 36.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 1984.0,
'NumberOfMissingValues': 15419.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeature... | APSFailure_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"ac_000",
"af_000",
"ag_001",
"ag_003",
"ag_004",
"ag_005",
"ag_006",
"ag_007",
"ag_009",
"ak_000",
"al_000",
"am_0",
"an_000",
"ap_000",
"at_000",
"av_000",
"ax_000",
"ay_001",
"ay_002",
"ay_003",
"ay_004",
"ay_006",
"ay_008",
"ay_009",
"az_000",
"az_001",
"az_00... | [
false,
false,
false,
false,
false,
false,
false,
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false,
false,
false,
false,
false,
false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 3,282 |
362,789 | predictive_accuracy | accuracy_score | ozone-level-8hr_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset ozone-level-8hr (1487) 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_ma... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 1874.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 126.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 73.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 72.0,
'NumberOfSymbolicFeatures': 1.0,... | ozone-level-8hr_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27",
"V28",
"V29",
"V30",
"V31",
"V32",
"V33",
"V34",
"V35",
"V36",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 3,283 |
362,844 | predictive_accuracy | accuracy_score | qsar-biodeg_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset qsar-biodeg (1494) with
seed=1
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: i... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 699.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 356.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 42.0,
'NumberOfInstances': 1055.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 41.0,
'NumberOfSymbolicFeatures': 1.0,
... | qsar-biodeg_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27",
"V28",
"V29",
"V30",
"V31",
"V32",
"V33",
"V34",
"V35",
"V36",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 3,284 |
4,635 | predictive_accuracy | accuracy_score | mouseType | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Data from the RSCTC 2010 Discovery Challenge. All datasets contain between 100 and 400 samples, characterized by values of 20,000 - 65,000 attributes. Samples are assigned to several (2-10) classes. All attributes are numeric and represent measure... | {0: [0 - Var1 (numeric)],
1: [1 - Var2 (numeric)],
2: [2 - Var3 (numeric)],
3: [3 - Var4 (numeric)],
4: [4 - Var5 (numeric)],
5: [5 - Var6 (numeric)],
6: [6 - Var7 (numeric)],
7: [7 - Var8 (numeric)],
8: [8 - Var9 (numeric)],
9: [9 - Var10 (numeric)],
10: [10 - Var11 (numeric)],
11: [11 - Var12 (numeric)],
... | {'MajorityClassSize': 69.0,
'MaxNominalAttDistinctValues': 7.0,
'MinorityClassSize': 13.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 45102.0,
'NumberOfInstances': 214.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 45101.0,
'NumberOfSymbolicFeatures': 1.... | mouseType | [
"Var1",
"Var2",
"Var3",
"Var4",
"Var5",
"Var6",
"Var7",
"Var8",
"Var9",
"Var10",
"Var11",
"Var12",
"Var13",
"Var14",
"Var15",
"Var16",
"Var17",
"Var18",
"Var19",
"Var20",
"Var21",
"Var22",
"Var23",
"Var24",
"Var25",
"Var26",
"Var27",
"Var28",
"Var29",
"Var30... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 3,285 |
362,879 | predictive_accuracy | accuracy_score | segment_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset segment (40984) 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 - short.line.density.5 (numeric)],
1: [1 - short.line.density.2 (numeric)],
2: [2 - vedge.mean (numeric)],
3: [3 - vegde.sd (numeric)],
4: [4 - hedge.mean (numeric)],
5: [5 - hedge.sd (numeric)],
6: [6 - intensity.mean (numeric)],
7: [7 - rawred.mean (numeric)],
8: [8 - rawblue.mean (numeric)],
9: [9 - ... | {'MajorityClassSize': 286.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 285.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 17.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 16.0,
'NumberOfSymbolicFeatures': 1.0,
... | segment_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"short.line.density.5",
"short.line.density.2",
"vedge.mean",
"vegde.sd",
"hedge.mean",
"hedge.sd",
"intensity.mean",
"rawred.mean",
"rawblue.mean",
"rawgreen.mean",
"exred.mean",
"exblue.mean",
"exgreen.mean",
"value.mean",
"saturation.mean",
"hue.mean"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,286 |
362,876 | predictive_accuracy | accuracy_score | kc1_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset kc1 (1067) 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 = 10,... | {0: [0 - loc (numeric)],
1: [1 - v(g) (numeric)],
2: [2 - ev(g) (numeric)],
3: [3 - iv(g) (numeric)],
4: [4 - n (numeric)],
5: [5 - v (numeric)],
6: [6 - l (numeric)],
7: [7 - d (numeric)],
8: [8 - i (numeric)],
9: [9 - e (numeric)],
10: [10 - b (numeric)],
11: [11 - t (numeric)],
12: [12 - lOCode (numeric)... | {'MajorityClassSize': 1691.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 309.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 22.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 21.0,
'NumberOfSymbolicFeatures': 1.0,... | kc1_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"loc",
"v(g)",
"ev(g)",
"iv(g)",
"n",
"v",
"l",
"d",
"i",
"e",
"b",
"t",
"lOCode",
"lOComment",
"lOBlank",
"locCodeAndComment",
"uniq_Op",
"uniq_Opnd",
"total_Op",
"total_Opnd",
"branchCount"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,287 |
362,877 | predictive_accuracy | accuracy_score | kc1_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset kc1 (1067) 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 = 10,... | {0: [0 - loc (numeric)],
1: [1 - v(g) (numeric)],
2: [2 - ev(g) (numeric)],
3: [3 - iv(g) (numeric)],
4: [4 - n (numeric)],
5: [5 - v (numeric)],
6: [6 - l (numeric)],
7: [7 - d (numeric)],
8: [8 - i (numeric)],
9: [9 - e (numeric)],
10: [10 - b (numeric)],
11: [11 - t (numeric)],
12: [12 - lOCode (numeric)... | {'MajorityClassSize': 1691.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 309.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 22.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 21.0,
'NumberOfSymbolicFeatures': 1.0,... | kc1_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"loc",
"v(g)",
"ev(g)",
"iv(g)",
"n",
"v",
"l",
"d",
"i",
"e",
"b",
"t",
"lOCode",
"lOComment",
"lOBlank",
"locCodeAndComment",
"uniq_Op",
"uniq_Opnd",
"total_Op",
"total_Opnd",
"branchCount"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,288 |
362,753 | predictive_accuracy | accuracy_score | APSFailure_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset APSFailure (41138) with
seed=0
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: i... | {0: [0 - aa_000 (numeric)],
1: [1 - ab_000 (numeric)],
2: [2 - ac_000 (numeric)],
3: [3 - ad_000 (numeric)],
4: [4 - af_000 (numeric)],
5: [5 - ag_002 (numeric)],
6: [6 - ag_003 (numeric)],
7: [7 - ag_004 (numeric)],
8: [8 - ag_007 (numeric)],
9: [9 - ag_008 (numeric)],
10: [10 - ai_000 (numeric)],
11: [11 -... | {'MajorityClassSize': 1964.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 36.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 1974.0,
'NumberOfMissingValues': 19825.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeature... | APSFailure_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"aa_000",
"ab_000",
"ac_000",
"ad_000",
"af_000",
"ag_002",
"ag_003",
"ag_004",
"ag_007",
"ag_008",
"ai_000",
"aj_000",
"ak_000",
"ao_000",
"aq_000",
"av_000",
"ay_000",
"ay_005",
"ay_006",
"ay_009",
"az_001",
"az_002",
"az_003",
"az_004",
"az_005",
"az_006",
"az_... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,289 |
362,875 | predictive_accuracy | accuracy_score | kc1_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset kc1 (1067) 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 = 10,... | {0: [0 - loc (numeric)],
1: [1 - v(g) (numeric)],
2: [2 - ev(g) (numeric)],
3: [3 - iv(g) (numeric)],
4: [4 - n (numeric)],
5: [5 - v (numeric)],
6: [6 - l (numeric)],
7: [7 - d (numeric)],
8: [8 - i (numeric)],
9: [9 - e (numeric)],
10: [10 - b (numeric)],
11: [11 - t (numeric)],
12: [12 - lOCode (numeric)... | {'MajorityClassSize': 1691.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 309.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 22.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 21.0,
'NumberOfSymbolicFeatures': 1.0,... | kc1_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"loc",
"v(g)",
"ev(g)",
"iv(g)",
"n",
"v",
"l",
"d",
"i",
"e",
"b",
"t",
"lOCode",
"lOComment",
"lOBlank",
"locCodeAndComment",
"uniq_Op",
"uniq_Opnd",
"total_Op",
"total_Opnd",
"branchCount"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,290 |
362,874 | predictive_accuracy | accuracy_score | kc1_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset kc1 (1067) 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 = 10,... | {0: [0 - loc (numeric)],
1: [1 - v(g) (numeric)],
2: [2 - ev(g) (numeric)],
3: [3 - iv(g) (numeric)],
4: [4 - n (numeric)],
5: [5 - v (numeric)],
6: [6 - l (numeric)],
7: [7 - d (numeric)],
8: [8 - i (numeric)],
9: [9 - e (numeric)],
10: [10 - b (numeric)],
11: [11 - t (numeric)],
12: [12 - lOCode (numeric)... | {'MajorityClassSize': 1691.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 309.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 22.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 21.0,
'NumberOfSymbolicFeatures': 1.0,... | kc1_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"loc",
"v(g)",
"ev(g)",
"iv(g)",
"n",
"v",
"l",
"d",
"i",
"e",
"b",
"t",
"lOCode",
"lOComment",
"lOBlank",
"locCodeAndComment",
"uniq_Op",
"uniq_Opnd",
"total_Op",
"total_Opnd",
"branchCount"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,291 |
362,880 | predictive_accuracy | accuracy_score | segment_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset segment (40984) 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 - short.line.density.5 (numeric)],
1: [1 - short.line.density.2 (numeric)],
2: [2 - vedge.mean (numeric)],
3: [3 - vegde.sd (numeric)],
4: [4 - hedge.mean (numeric)],
5: [5 - hedge.sd (numeric)],
6: [6 - intensity.mean (numeric)],
7: [7 - rawred.mean (numeric)],
8: [8 - rawblue.mean (numeric)],
9: [9 - ... | {'MajorityClassSize': 286.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 285.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 17.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 16.0,
'NumberOfSymbolicFeatures': 1.0,
... | segment_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"short.line.density.5",
"short.line.density.2",
"vedge.mean",
"vegde.sd",
"hedge.mean",
"hedge.sd",
"intensity.mean",
"rawred.mean",
"rawblue.mean",
"rawgreen.mean",
"exred.mean",
"exblue.mean",
"exgreen.mean",
"value.mean",
"saturation.mean",
"hue.mean"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,292 |
362,881 | predictive_accuracy | accuracy_score | segment_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset segment (40984) 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 - short.line.density.5 (numeric)],
1: [1 - short.line.density.2 (numeric)],
2: [2 - vedge.mean (numeric)],
3: [3 - vegde.sd (numeric)],
4: [4 - hedge.mean (numeric)],
5: [5 - hedge.sd (numeric)],
6: [6 - intensity.mean (numeric)],
7: [7 - rawred.mean (numeric)],
8: [8 - rawblue.mean (numeric)],
9: [9 - ... | {'MajorityClassSize': 286.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 285.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 17.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 16.0,
'NumberOfSymbolicFeatures': 1.0,
... | segment_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"short.line.density.5",
"short.line.density.2",
"vedge.mean",
"vegde.sd",
"hedge.mean",
"hedge.sd",
"intensity.mean",
"rawred.mean",
"rawblue.mean",
"rawgreen.mean",
"exred.mean",
"exblue.mean",
"exgreen.mean",
"value.mean",
"saturation.mean",
"hue.mean"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,293 |
362,882 | predictive_accuracy | accuracy_score | segment_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset segment (40984) 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 - short.line.density.5 (numeric)],
1: [1 - short.line.density.2 (numeric)],
2: [2 - vedge.mean (numeric)],
3: [3 - vegde.sd (numeric)],
4: [4 - hedge.mean (numeric)],
5: [5 - hedge.sd (numeric)],
6: [6 - intensity.mean (numeric)],
7: [7 - rawred.mean (numeric)],
8: [8 - rawblue.mean (numeric)],
9: [9 - ... | {'MajorityClassSize': 286.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 285.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 17.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 16.0,
'NumberOfSymbolicFeatures': 1.0,
... | segment_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"short.line.density.5",
"short.line.density.2",
"vedge.mean",
"vegde.sd",
"hedge.mean",
"hedge.sd",
"intensity.mean",
"rawred.mean",
"rawblue.mean",
"rawgreen.mean",
"exred.mean",
"exblue.mean",
"exgreen.mean",
"value.mean",
"saturation.mean",
"hue.mean"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,294 |
362,891 | predictive_accuracy | accuracy_score | kr-vs-kp_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset kr-vs-kp (3) 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 - bkblk (nominal)],
1: [1 - bknwy (nominal)],
2: [2 - bkon8 (nominal)],
3: [3 - bkona (nominal)],
4: [4 - bkspr (nominal)],
5: [5 - bkxbq (nominal)],
6: [6 - bkxcr (nominal)],
7: [7 - bkxwp (nominal)],
8: [8 - blxwp (nominal)],
9: [9 - bxqsq (nominal)],
10: [10 - cntxt (nominal)],
11: [11 - dsopp (nom... | {'MajorityClassSize': 1044.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 956.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 37.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 37.0,... | kr-vs-kp_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"bkblk",
"bknwy",
"bkon8",
"bkona",
"bkspr",
"bkxbq",
"bkxcr",
"bkxwp",
"blxwp",
"bxqsq",
"cntxt",
"dsopp",
"dwipd",
"hdchk",
"katri",
"mulch",
"qxmsq",
"r2ar8",
"reskd",
"reskr",
"rimmx",
"rkxwp",
"rxmsq",
"simpl",
"skach",
"skewr",
"skrxp",
"spcop",
"stlmt",... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,295 |
362,890 | predictive_accuracy | accuracy_score | kr-vs-kp_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset kr-vs-kp (3) 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 - bkblk (nominal)],
1: [1 - bknwy (nominal)],
2: [2 - bkon8 (nominal)],
3: [3 - bkona (nominal)],
4: [4 - bkspr (nominal)],
5: [5 - bkxbq (nominal)],
6: [6 - bkxcr (nominal)],
7: [7 - bkxwp (nominal)],
8: [8 - blxwp (nominal)],
9: [9 - bxqsq (nominal)],
10: [10 - cntxt (nominal)],
11: [11 - dsopp (nom... | {'MajorityClassSize': 1044.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 956.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 37.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 37.0,... | kr-vs-kp_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"bkblk",
"bknwy",
"bkon8",
"bkona",
"bkspr",
"bkxbq",
"bkxcr",
"bkxwp",
"blxwp",
"bxqsq",
"cntxt",
"dsopp",
"dwipd",
"hdchk",
"katri",
"mulch",
"qxmsq",
"r2ar8",
"reskd",
"reskr",
"rimmx",
"rkxwp",
"rxmsq",
"simpl",
"skach",
"skewr",
"skrxp",
"spcop",
"stlmt",... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,296 |
362,756 | predictive_accuracy | accuracy_score | APSFailure_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset APSFailure (41138) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: i... | {0: [0 - aa_000 (numeric)],
1: [1 - ac_000 (numeric)],
2: [2 - ad_000 (numeric)],
3: [3 - af_000 (numeric)],
4: [4 - ag_000 (numeric)],
5: [5 - ag_001 (numeric)],
6: [6 - ag_002 (numeric)],
7: [7 - ag_004 (numeric)],
8: [8 - ag_006 (numeric)],
9: [9 - ag_007 (numeric)],
10: [10 - ah_000 (numeric)],
11: [11 -... | {'MajorityClassSize': 1964.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 36.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 1885.0,
'NumberOfMissingValues': 15654.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeature... | APSFailure_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"aa_000",
"ac_000",
"ad_000",
"af_000",
"ag_000",
"ag_001",
"ag_002",
"ag_004",
"ag_006",
"ag_007",
"ah_000",
"ai_000",
"an_000",
"ao_000",
"ar_000",
"au_000",
"av_000",
"ay_000",
"ay_001",
"ay_002",
"ay_003",
"ay_005",
"ay_007",
"ay_008",
"ay_009",
"az_000",
"az_... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,297 |
362,766 | predictive_accuracy | accuracy_score | APSFailure_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset APSFailure (41138) with
seed=2
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: i... | {0: [0 - ae_000 (numeric)],
1: [1 - ag_000 (numeric)],
2: [2 - ag_001 (numeric)],
3: [3 - ag_003 (numeric)],
4: [4 - ag_005 (numeric)],
5: [5 - ag_007 (numeric)],
6: [6 - ag_008 (numeric)],
7: [7 - ag_009 (numeric)],
8: [8 - ah_000 (numeric)],
9: [9 - ai_000 (numeric)],
10: [10 - aj_000 (numeric)],
11: [11 -... | {'MajorityClassSize': 1964.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 36.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 1985.0,
'NumberOfMissingValues': 17956.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeature... | APSFailure_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"ae_000",
"ag_000",
"ag_001",
"ag_003",
"ag_005",
"ag_007",
"ag_008",
"ag_009",
"ah_000",
"ai_000",
"aj_000",
"al_000",
"an_000",
"ap_000",
"ar_000",
"as_000",
"ax_000",
"ay_002",
"ay_003",
"ay_004",
"ay_006",
"ay_008",
"ay_009",
"az_000",
"az_001",
"az_006",
"az_... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,298 |
362,888 | predictive_accuracy | accuracy_score | kr-vs-kp_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset kr-vs-kp (3) 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 - bkblk (nominal)],
1: [1 - bknwy (nominal)],
2: [2 - bkon8 (nominal)],
3: [3 - bkona (nominal)],
4: [4 - bkspr (nominal)],
5: [5 - bkxbq (nominal)],
6: [6 - bkxcr (nominal)],
7: [7 - bkxwp (nominal)],
8: [8 - blxwp (nominal)],
9: [9 - bxqsq (nominal)],
10: [10 - cntxt (nominal)],
11: [11 - dsopp (nom... | {'MajorityClassSize': 1044.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 956.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 37.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 37.0,... | kr-vs-kp_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"bkblk",
"bknwy",
"bkon8",
"bkona",
"bkspr",
"bkxbq",
"bkxcr",
"bkxwp",
"blxwp",
"bxqsq",
"cntxt",
"dsopp",
"dwipd",
"hdchk",
"katri",
"mulch",
"qxmsq",
"r2ar8",
"reskd",
"reskr",
"rimmx",
"rkxwp",
"rxmsq",
"simpl",
"skach",
"skewr",
"skrxp",
"spcop",
"stlmt",... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,299 |
362,728 | predictive_accuracy | accuracy_score | KDDCup09_appetency_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset KDDCup09_appetency (1111) 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... | {0: [0 - Var6 (numeric)],
1: [1 - Var10 (numeric)],
2: [2 - Var12 (numeric)],
3: [3 - Var16 (numeric)],
4: [4 - Var22 (numeric)],
5: [5 - Var24 (numeric)],
6: [6 - Var26 (numeric)],
7: [7 - Var30 (numeric)],
8: [8 - Var33 (numeric)],
9: [9 - Var37 (numeric)],
10: [10 - Var38 (numeric)],
11: [11 - Var40 (nume... | {'MajorityClassSize': 1964.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 36.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 94.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 2000.0,
'NumberOfMissingValues': 124468.0,
'NumberOfNumericFeatures': 72.0,
'NumberOfSymbolicFeatures... | KDDCup09_appetency_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Var6",
"Var10",
"Var12",
"Var16",
"Var22",
"Var24",
"Var26",
"Var30",
"Var33",
"Var37",
"Var38",
"Var40",
"Var43",
"Var46",
"Var50",
"Var54",
"Var60",
"Var64",
"Var66",
"Var68",
"Var69",
"Var71",
"Var74",
"Var77",
"Var80",
"Var82",
"Var85",
"Var86",
"Var88",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,300 |
362,755 | predictive_accuracy | accuracy_score | APSFailure_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset APSFailure (41138) with
seed=4
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: i... | {0: [0 - ad_000 (numeric)],
1: [1 - af_000 (numeric)],
2: [2 - ag_000 (numeric)],
3: [3 - ag_004 (numeric)],
4: [4 - ag_005 (numeric)],
5: [5 - ag_007 (numeric)],
6: [6 - ag_009 (numeric)],
7: [7 - ah_000 (numeric)],
8: [8 - ak_000 (numeric)],
9: [9 - am_0 (numeric)],
10: [10 - an_000 (numeric)],
11: [11 - a... | {'MajorityClassSize': 1964.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 36.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 1975.0,
'NumberOfMissingValues': 19156.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeature... | APSFailure_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"ad_000",
"af_000",
"ag_000",
"ag_004",
"ag_005",
"ag_007",
"ag_009",
"ah_000",
"ak_000",
"am_0",
"an_000",
"ao_000",
"aq_000",
"as_000",
"au_000",
"av_000",
"ay_000",
"ay_001",
"ay_003",
"ay_004",
"ay_005",
"ay_009",
"az_002",
"az_003",
"az_004",
"az_006",
"az_00... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,301 |
362,889 | predictive_accuracy | accuracy_score | kr-vs-kp_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset kr-vs-kp (3) 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 - bkblk (nominal)],
1: [1 - bknwy (nominal)],
2: [2 - bkon8 (nominal)],
3: [3 - bkona (nominal)],
4: [4 - bkspr (nominal)],
5: [5 - bkxbq (nominal)],
6: [6 - bkxcr (nominal)],
7: [7 - bkxwp (nominal)],
8: [8 - blxwp (nominal)],
9: [9 - bxqsq (nominal)],
10: [10 - cntxt (nominal)],
11: [11 - dsopp (nom... | {'MajorityClassSize': 1044.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 956.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 37.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 37.0,... | kr-vs-kp_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"bkblk",
"bknwy",
"bkon8",
"bkona",
"bkspr",
"bkxbq",
"bkxcr",
"bkxwp",
"blxwp",
"bxqsq",
"cntxt",
"dsopp",
"dwipd",
"hdchk",
"katri",
"mulch",
"qxmsq",
"r2ar8",
"reskd",
"reskr",
"rimmx",
"rkxwp",
"rxmsq",
"simpl",
"skach",
"skewr",
"skrxp",
"spcop",
"stlmt",... | [
true,
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true,
true,
true,
true,
true,
true,
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true,
true,
true,
true,
true,
true,
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true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,302 |
362,893 | predictive_accuracy | accuracy_score | kr-vs-kp_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset kr-vs-kp (3) with
seed=4
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int = 1... | {0: [0 - bkblk (nominal)],
1: [1 - bknwy (nominal)],
2: [2 - bkon8 (nominal)],
3: [3 - bkona (nominal)],
4: [4 - bkspr (nominal)],
5: [5 - bkxbq (nominal)],
6: [6 - bkxcr (nominal)],
7: [7 - bkxwp (nominal)],
8: [8 - blxwp (nominal)],
9: [9 - bxqsq (nominal)],
10: [10 - cntxt (nominal)],
11: [11 - dsopp (nom... | {'MajorityClassSize': 1044.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 956.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 37.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 37.0,... | kr-vs-kp_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"bkblk",
"bknwy",
"bkon8",
"bkona",
"bkspr",
"bkxbq",
"bkxcr",
"bkxwp",
"blxwp",
"bxqsq",
"cntxt",
"dsopp",
"dwipd",
"hdchk",
"katri",
"mulch",
"qxmsq",
"r2ar8",
"reskd",
"reskr",
"rimmx",
"rkxwp",
"rxmsq",
"simpl",
"skach",
"skewr",
"skrxp",
"spcop",
"stlmt",... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,303 |
362,799 | predictive_accuracy | accuracy_score | madeline_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset madeline (41144) 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 - V5 (numeric)],
4: [4 - V6 (numeric)],
5: [5 - V7 (numeric)],
6: [6 - V13 (numeric)],
7: [7 - V16 (numeric)],
8: [8 - V18 (numeric)],
9: [9 - V22 (numeric)],
10: [10 - V26 (numeric)],
11: [11 - V30 (numeric)],
12: [12 - V31 (numeri... | {'MajorityClassSize': 1006.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 994.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | madeline_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V5",
"V6",
"V7",
"V13",
"V16",
"V18",
"V22",
"V26",
"V30",
"V31",
"V34",
"V44",
"V50",
"V51",
"V54",
"V58",
"V60",
"V62",
"V67",
"V72",
"V73",
"V79",
"V81",
"V82",
"V83",
"V84",
"V85",
"V86",
"V87",
"V96",
"V97",
"V98",
"V99",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 3,304 |
362,801 | predictive_accuracy | accuracy_score | madeline_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset madeline (41144) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V6 (numeric)],
3: [3 - V7 (numeric)],
4: [4 - V9 (numeric)],
5: [5 - V14 (numeric)],
6: [6 - V16 (numeric)],
7: [7 - V21 (numeric)],
8: [8 - V27 (numeric)],
9: [9 - V28 (numeric)],
10: [10 - V29 (numeric)],
11: [11 - V30 (numeric)],
12: [12 - V33 (numer... | {'MajorityClassSize': 1006.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 994.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | madeline_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V6",
"V7",
"V9",
"V14",
"V16",
"V21",
"V27",
"V28",
"V29",
"V30",
"V33",
"V39",
"V41",
"V47",
"V48",
"V52",
"V53",
"V55",
"V56",
"V57",
"V58",
"V59",
"V62",
"V63",
"V65",
"V70",
"V71",
"V75",
"V78",
"V80",
"V82",
"V83",
"V94",
"V95",... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 3,305 |
362,869 | predictive_accuracy | accuracy_score | mfeat-factors_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset mfeat-factors (12) with
seed=1
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: i... | {0: [0 - att4 (numeric)],
1: [1 - att5 (numeric)],
2: [2 - att8 (numeric)],
3: [3 - att10 (numeric)],
4: [4 - att12 (numeric)],
5: [5 - att13 (numeric)],
6: [6 - att16 (numeric)],
7: [7 - att18 (numeric)],
8: [8 - att19 (numeric)],
9: [9 - att20 (numeric)],
10: [10 - att21 (numeric)],
11: [11 - att28 (numeri... | {'MajorityClassSize': 200.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 200.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | mfeat-factors_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"att4",
"att5",
"att8",
"att10",
"att12",
"att13",
"att16",
"att18",
"att19",
"att20",
"att21",
"att28",
"att32",
"att34",
"att36",
"att40",
"att41",
"att44",
"att45",
"att47",
"att48",
"att50",
"att53",
"att54",
"att55",
"att56",
"att57",
"att58",
"att61",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,306 |
362,798 | predictive_accuracy | accuracy_score | madeline_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset madeline (41144) 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 - V4 (numeric)],
1: [1 - V5 (numeric)],
2: [2 - V6 (numeric)],
3: [3 - V10 (numeric)],
4: [4 - V13 (numeric)],
5: [5 - V14 (numeric)],
6: [6 - V15 (numeric)],
7: [7 - V16 (numeric)],
8: [8 - V20 (numeric)],
9: [9 - V24 (numeric)],
10: [10 - V25 (numeric)],
11: [11 - V26 (numeric)],
12: [12 - V35 (num... | {'MajorityClassSize': 1006.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 994.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | madeline_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V4",
"V5",
"V6",
"V10",
"V13",
"V14",
"V15",
"V16",
"V20",
"V24",
"V25",
"V26",
"V35",
"V39",
"V41",
"V42",
"V45",
"V47",
"V53",
"V57",
"V58",
"V60",
"V62",
"V63",
"V65",
"V66",
"V71",
"V72",
"V73",
"V75",
"V76",
"V79",
"V83",
"V85",
"V86",
"V87... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,307 |
362,905 | predictive_accuracy | accuracy_score | churn_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset churn (40701) 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 - state (numeric)],
1: [1 - account_length (numeric)],
2: [2 - area_code (nominal)],
3: [3 - phone_number (numeric)],
4: [4 - international_plan (nominal)],
5: [5 - voice_mail_plan (nominal)],
6: [6 - number_vmail_messages (numeric)],
7: [7 - total_day_minutes (numeric)],
8: [8 - total_day_calls (numeric... | {'MajorityClassSize': 1717.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 283.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 16.0,
'NumberOfSymbolicFeatures': 5.0,... | churn_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"state",
"account_length",
"area_code",
"phone_number",
"international_plan",
"voice_mail_plan",
"number_vmail_messages",
"total_day_minutes",
"total_day_calls",
"total_day_charge",
"total_eve_minutes",
"total_eve_calls",
"total_eve_charge",
"total_night_minutes",
"total_night_calls",
... | [
false,
false,
true,
false,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 3,308 |
359,953 | predictive_accuracy | accuracy_score | micro-mass | **Author**: Pierre Mahé, Jean-Baptiste Veyrieras
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/MicroMass) - 2014
**Please cite**:
### Description
MicroMass (pure spectra version) is a dataset to explore machine learning approaches for the identification of microorganisms from mass-spectrometry data... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 60.0,
'MaxNominalAttDistinctValues': 20.0,
'MinorityClassSize': 11.0,
'NumberOfClasses': 20.0,
'NumberOfFeatures': 1301.0,
'NumberOfInstances': 571.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1300.0,
'NumberOfSymbolicFeatures': 1.... | micro-mass | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27",
"V28",
"V29",
"V30",
"V31",
"V32",
"V33",
"V34",
"V35",
"V36",
"... | [
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false,
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f... | 3,309 |
362,800 | predictive_accuracy | accuracy_score | madeline_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset madeline (41144) 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 - V8 (numeric)],
1: [1 - V10 (numeric)],
2: [2 - V15 (numeric)],
3: [3 - V16 (numeric)],
4: [4 - V18 (numeric)],
5: [5 - V19 (numeric)],
6: [6 - V23 (numeric)],
7: [7 - V24 (numeric)],
8: [8 - V26 (numeric)],
9: [9 - V28 (numeric)],
10: [10 - V33 (numeric)],
11: [11 - V36 (numeric)],
12: [12 - V38 (n... | {'MajorityClassSize': 1006.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 994.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | madeline_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V8",
"V10",
"V15",
"V16",
"V18",
"V19",
"V23",
"V24",
"V26",
"V28",
"V33",
"V36",
"V38",
"V42",
"V43",
"V45",
"V48",
"V49",
"V51",
"V56",
"V57",
"V67",
"V68",
"V70",
"V75",
"V78",
"V80",
"V81",
"V86",
"V94",
"V99",
"V100",
"V101",
"V102",
"V104",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,310 |
362,867 | predictive_accuracy | accuracy_score | mfeat-factors_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset mfeat-factors (12) with
seed=0
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: i... | {0: [0 - att1 (numeric)],
1: [1 - att2 (numeric)],
2: [2 - att3 (numeric)],
3: [3 - att4 (numeric)],
4: [4 - att5 (numeric)],
5: [5 - att10 (numeric)],
6: [6 - att11 (numeric)],
7: [7 - att13 (numeric)],
8: [8 - att14 (numeric)],
9: [9 - att15 (numeric)],
10: [10 - att18 (numeric)],
11: [11 - att20 (numeric)... | {'MajorityClassSize': 200.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 200.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | mfeat-factors_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"att1",
"att2",
"att3",
"att4",
"att5",
"att10",
"att11",
"att13",
"att14",
"att15",
"att18",
"att20",
"att22",
"att25",
"att26",
"att33",
"att38",
"att43",
"att47",
"att48",
"att51",
"att56",
"att59",
"att61",
"att64",
"att65",
"att66",
"att68",
"att69",
"a... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,311 |
362,872 | predictive_accuracy | accuracy_score | mfeat-factors_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset mfeat-factors (12) with
seed=4
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: i... | {0: [0 - att6 (numeric)],
1: [1 - att8 (numeric)],
2: [2 - att9 (numeric)],
3: [3 - att11 (numeric)],
4: [4 - att15 (numeric)],
5: [5 - att20 (numeric)],
6: [6 - att22 (numeric)],
7: [7 - att23 (numeric)],
8: [8 - att24 (numeric)],
9: [9 - att28 (numeric)],
10: [10 - att30 (numeric)],
11: [11 - att34 (numeri... | {'MajorityClassSize': 200.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 200.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | mfeat-factors_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"att6",
"att8",
"att9",
"att11",
"att15",
"att20",
"att22",
"att23",
"att24",
"att28",
"att30",
"att34",
"att36",
"att39",
"att40",
"att43",
"att47",
"att48",
"att49",
"att50",
"att55",
"att57",
"att61",
"att62",
"att63",
"att65",
"att67",
"att72",
"att74",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,312 |
362,883 | predictive_accuracy | accuracy_score | dna_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset dna (40670) 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 = 10... | {0: [0 - A0 (nominal)],
1: [1 - A1 (nominal)],
2: [2 - A2 (nominal)],
3: [3 - A3 (nominal)],
4: [4 - A6 (nominal)],
5: [5 - A8 (nominal)],
6: [6 - A9 (nominal)],
7: [7 - A10 (nominal)],
8: [8 - A11 (nominal)],
9: [9 - A14 (nominal)],
10: [10 - A15 (nominal)],
11: [11 - A19 (nominal)],
12: [12 - A22 (nominal... | {'MajorityClassSize': 1038.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 480.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 101.... | dna_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"A0",
"A1",
"A2",
"A3",
"A6",
"A8",
"A9",
"A10",
"A11",
"A14",
"A15",
"A19",
"A22",
"A26",
"A28",
"A33",
"A35",
"A39",
"A41",
"A42",
"A45",
"A46",
"A47",
"A48",
"A50",
"A51",
"A52",
"A53",
"A55",
"A56",
"A57",
"A59",
"A61",
"A62",
"A64",
"A66",
... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true... | 3,313 |
362,918 | predictive_accuracy | accuracy_score | PhishingWebsites_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset PhishingWebsites (4534) 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_m... | {0: [0 - having_IP_Address (nominal)],
1: [1 - URL_Length (nominal)],
2: [2 - Shortining_Service (nominal)],
3: [3 - having_At_Symbol (nominal)],
4: [4 - double_slash_redirecting (nominal)],
5: [5 - Prefix_Suffix (nominal)],
6: [6 - having_Sub_Domain (nominal)],
7: [7 - SSLfinal_State (nominal)],
8: [8 - Domain... | {'MajorityClassSize': 1114.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 886.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 31.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 31.0,... | PhishingWebsites_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"having_IP_Address",
"URL_Length",
"Shortining_Service",
"having_At_Symbol",
"double_slash_redirecting",
"Prefix_Suffix",
"having_Sub_Domain",
"SSLfinal_State",
"Domain_registeration_length",
"Favicon",
"port",
"HTTPS_token",
"Request_URL",
"URL_of_Anchor",
"Links_in_tags",
"SFH",
"S... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,314 |
362,906 | predictive_accuracy | accuracy_score | churn_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset churn (40701) 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 - state (numeric)],
1: [1 - account_length (numeric)],
2: [2 - area_code (nominal)],
3: [3 - phone_number (numeric)],
4: [4 - international_plan (nominal)],
5: [5 - voice_mail_plan (nominal)],
6: [6 - number_vmail_messages (numeric)],
7: [7 - total_day_minutes (numeric)],
8: [8 - total_day_calls (numeric... | {'MajorityClassSize': 1717.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 283.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 16.0,
'NumberOfSymbolicFeatures': 5.0,... | churn_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"state",
"account_length",
"area_code",
"phone_number",
"international_plan",
"voice_mail_plan",
"number_vmail_messages",
"total_day_minutes",
"total_day_calls",
"total_day_charge",
"total_eve_minutes",
"total_eve_calls",
"total_eve_charge",
"total_night_minutes",
"total_night_calls",
... | [
false,
false,
true,
false,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 3,315 |
362,803 | predictive_accuracy | accuracy_score | madeline_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset madeline (41144) 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 - V7 (numeric)],
1: [1 - V9 (numeric)],
2: [2 - V12 (numeric)],
3: [3 - V14 (numeric)],
4: [4 - V19 (numeric)],
5: [5 - V26 (numeric)],
6: [6 - V28 (numeric)],
7: [7 - V31 (numeric)],
8: [8 - V33 (numeric)],
9: [9 - V36 (numeric)],
10: [10 - V37 (numeric)],
11: [11 - V40 (numeric)],
12: [12 - V43 (nu... | {'MajorityClassSize': 1006.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 994.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | madeline_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V7",
"V9",
"V12",
"V14",
"V19",
"V26",
"V28",
"V31",
"V33",
"V36",
"V37",
"V40",
"V43",
"V44",
"V47",
"V49",
"V50",
"V52",
"V57",
"V61",
"V62",
"V65",
"V71",
"V76",
"V77",
"V78",
"V80",
"V83",
"V84",
"V88",
"V91",
"V92",
"V98",
"V100",
"V102",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,316 |
362,870 | predictive_accuracy | accuracy_score | mfeat-factors_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset mfeat-factors (12) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: i... | {0: [0 - att1 (numeric)],
1: [1 - att5 (numeric)],
2: [2 - att6 (numeric)],
3: [3 - att8 (numeric)],
4: [4 - att11 (numeric)],
5: [5 - att12 (numeric)],
6: [6 - att13 (numeric)],
7: [7 - att16 (numeric)],
8: [8 - att17 (numeric)],
9: [9 - att22 (numeric)],
10: [10 - att23 (numeric)],
11: [11 - att26 (numeric... | {'MajorityClassSize': 200.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 200.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | mfeat-factors_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"att1",
"att5",
"att6",
"att8",
"att11",
"att12",
"att13",
"att16",
"att17",
"att22",
"att23",
"att26",
"att29",
"att33",
"att35",
"att39",
"att42",
"att43",
"att45",
"att46",
"att47",
"att49",
"att52",
"att54",
"att56",
"att57",
"att60",
"att62",
"att63",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,317 |
362,892 | predictive_accuracy | accuracy_score | Internet-Advertisements_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Internet-Advertisements (40978) 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,
nc... | {0: [0 - url.images.buttons (nominal)],
1: [1 - url.oso (nominal)],
2: [2 - url.tkaine.kats (nominal)],
3: [3 - url.clawnext.gif (nominal)],
4: [4 - url.area51 (nominal)],
5: [5 - url.carousel.org (nominal)],
6: [6 - url.www.yahoo.co.uk (nominal)],
7: [7 - url.ads.switchboard.com (nominal)],
8: [8 - url.home.gi... | {'MajorityClassSize': 1720.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 280.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 101.... | Internet-Advertisements_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"url.images.buttons",
"url.oso",
"url.tkaine.kats",
"url.clawnext.gif",
"url.area51",
"url.carousel.org",
"url.www.yahoo.co.uk",
"url.ads.switchboard.com",
"url.home.gif",
"url.cjackson",
"url.labyrinth.9439",
"url.home",
"url.geoguideii.email",
"url.www.afn.org",
"url.icons",
"url.pag... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true... | 3,318 |
362,885 | predictive_accuracy | accuracy_score | dna_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset dna (40670) 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 = 10... | {0: [0 - A4 (nominal)],
1: [1 - A5 (nominal)],
2: [2 - A6 (nominal)],
3: [3 - A8 (nominal)],
4: [4 - A9 (nominal)],
5: [5 - A12 (nominal)],
6: [6 - A14 (nominal)],
7: [7 - A15 (nominal)],
8: [8 - A16 (nominal)],
9: [9 - A17 (nominal)],
10: [10 - A19 (nominal)],
11: [11 - A21 (nominal)],
12: [12 - A22 (nomin... | {'MajorityClassSize': 1038.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 480.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 101.... | dna_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"A4",
"A5",
"A6",
"A8",
"A9",
"A12",
"A14",
"A15",
"A16",
"A17",
"A19",
"A21",
"A22",
"A24",
"A25",
"A26",
"A28",
"A29",
"A30",
"A33",
"A35",
"A38",
"A39",
"A40",
"A41",
"A42",
"A44",
"A45",
"A46",
"A54",
"A57",
"A58",
"A61",
"A63",
"A64",
"A65",... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true... | 3,319 |
362,884 | predictive_accuracy | accuracy_score | dna_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset dna (40670) 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 = 10... | {0: [0 - A2 (nominal)],
1: [1 - A6 (nominal)],
2: [2 - A7 (nominal)],
3: [3 - A8 (nominal)],
4: [4 - A9 (nominal)],
5: [5 - A12 (nominal)],
6: [6 - A13 (nominal)],
7: [7 - A14 (nominal)],
8: [8 - A15 (nominal)],
9: [9 - A16 (nominal)],
10: [10 - A22 (nominal)],
11: [11 - A24 (nominal)],
12: [12 - A25 (nomin... | {'MajorityClassSize': 1038.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 480.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 101.... | dna_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"A2",
"A6",
"A7",
"A8",
"A9",
"A12",
"A13",
"A14",
"A15",
"A16",
"A22",
"A24",
"A25",
"A26",
"A28",
"A31",
"A33",
"A34",
"A35",
"A37",
"A38",
"A39",
"A41",
"A43",
"A44",
"A46",
"A48",
"A50",
"A53",
"A54",
"A55",
"A58",
"A59",
"A61",
"A62",
"A65",... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true... | 3,320 |
362,757 | predictive_accuracy | accuracy_score | dilbert_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset dilbert (41163) 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 - V6 (numeric)],
1: [1 - V11 (numeric)],
2: [2 - V17 (numeric)],
3: [3 - V32 (numeric)],
4: [4 - V43 (numeric)],
5: [5 - V55 (numeric)],
6: [6 - V65 (numeric)],
7: [7 - V79 (numeric)],
8: [8 - V97 (numeric)],
9: [9 - V144 (numeric)],
10: [10 - V145 (numeric)],
11: [11 - V156 (numeric)],
12: [12 - V16... | {'MajorityClassSize': 410.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 382.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0... | dilbert_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V6",
"V11",
"V17",
"V32",
"V43",
"V55",
"V65",
"V79",
"V97",
"V144",
"V145",
"V156",
"V168",
"V173",
"V242",
"V266",
"V335",
"V340",
"V453",
"V502",
"V505",
"V514",
"V527",
"V533",
"V581",
"V587",
"V612",
"V639",
"V654",
"V672",
"V709",
"V740",
"V743"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,321 |
362,871 | predictive_accuracy | accuracy_score | mfeat-factors_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset mfeat-factors (12) with
seed=2
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: i... | {0: [0 - att7 (numeric)],
1: [1 - att8 (numeric)],
2: [2 - att9 (numeric)],
3: [3 - att12 (numeric)],
4: [4 - att13 (numeric)],
5: [5 - att14 (numeric)],
6: [6 - att16 (numeric)],
7: [7 - att19 (numeric)],
8: [8 - att20 (numeric)],
9: [9 - att22 (numeric)],
10: [10 - att25 (numeric)],
11: [11 - att28 (numeri... | {'MajorityClassSize': 200.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 200.0,
'NumberOfClasses': 10.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | mfeat-factors_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"att7",
"att8",
"att9",
"att12",
"att13",
"att14",
"att16",
"att19",
"att20",
"att22",
"att25",
"att28",
"att30",
"att31",
"att33",
"att36",
"att37",
"att38",
"att42",
"att46",
"att51",
"att53",
"att55",
"att56",
"att59",
"att61",
"att62",
"att71",
"att75",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,322 |
362,903 | predictive_accuracy | accuracy_score | churn_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset churn (40701) 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 - state (numeric)],
1: [1 - account_length (numeric)],
2: [2 - area_code (nominal)],
3: [3 - phone_number (numeric)],
4: [4 - international_plan (nominal)],
5: [5 - voice_mail_plan (nominal)],
6: [6 - number_vmail_messages (numeric)],
7: [7 - total_day_minutes (numeric)],
8: [8 - total_day_calls (numeric... | {'MajorityClassSize': 1717.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 283.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 16.0,
'NumberOfSymbolicFeatures': 5.0,... | churn_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"state",
"account_length",
"area_code",
"phone_number",
"international_plan",
"voice_mail_plan",
"number_vmail_messages",
"total_day_minutes",
"total_day_calls",
"total_day_charge",
"total_eve_minutes",
"total_eve_calls",
"total_eve_charge",
"total_night_minutes",
"total_night_calls",
... | [
false,
false,
true,
false,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 3,323 |
362,919 | predictive_accuracy | accuracy_score | PhishingWebsites_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset PhishingWebsites (4534) 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_m... | {0: [0 - having_IP_Address (nominal)],
1: [1 - URL_Length (nominal)],
2: [2 - Shortining_Service (nominal)],
3: [3 - having_At_Symbol (nominal)],
4: [4 - double_slash_redirecting (nominal)],
5: [5 - Prefix_Suffix (nominal)],
6: [6 - having_Sub_Domain (nominal)],
7: [7 - SSLfinal_State (nominal)],
8: [8 - Domain... | {'MajorityClassSize': 1114.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 886.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 31.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 31.0,... | PhishingWebsites_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"having_IP_Address",
"URL_Length",
"Shortining_Service",
"having_At_Symbol",
"double_slash_redirecting",
"Prefix_Suffix",
"having_Sub_Domain",
"SSLfinal_State",
"Domain_registeration_length",
"Favicon",
"port",
"HTTPS_token",
"Request_URL",
"URL_of_Anchor",
"Links_in_tags",
"SFH",
"S... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,324 |
362,920 | predictive_accuracy | accuracy_score | PhishingWebsites_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset PhishingWebsites (4534) 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_m... | {0: [0 - having_IP_Address (nominal)],
1: [1 - URL_Length (nominal)],
2: [2 - Shortining_Service (nominal)],
3: [3 - having_At_Symbol (nominal)],
4: [4 - double_slash_redirecting (nominal)],
5: [5 - Prefix_Suffix (nominal)],
6: [6 - having_Sub_Domain (nominal)],
7: [7 - SSLfinal_State (nominal)],
8: [8 - Domain... | {'MajorityClassSize': 1114.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 886.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 31.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 31.0,... | PhishingWebsites_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"having_IP_Address",
"URL_Length",
"Shortining_Service",
"having_At_Symbol",
"double_slash_redirecting",
"Prefix_Suffix",
"having_Sub_Domain",
"SSLfinal_State",
"Domain_registeration_length",
"Favicon",
"port",
"HTTPS_token",
"Request_URL",
"URL_of_Anchor",
"Links_in_tags",
"SFH",
"S... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,325 |
362,904 | predictive_accuracy | accuracy_score | churn_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset churn (40701) 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 - state (numeric)],
1: [1 - account_length (numeric)],
2: [2 - area_code (nominal)],
3: [3 - phone_number (numeric)],
4: [4 - international_plan (nominal)],
5: [5 - voice_mail_plan (nominal)],
6: [6 - number_vmail_messages (numeric)],
7: [7 - total_day_minutes (numeric)],
8: [8 - total_day_calls (numeric... | {'MajorityClassSize': 1717.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 283.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 16.0,
'NumberOfSymbolicFeatures': 5.0,... | churn_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"state",
"account_length",
"area_code",
"phone_number",
"international_plan",
"voice_mail_plan",
"number_vmail_messages",
"total_day_minutes",
"total_day_calls",
"total_day_charge",
"total_eve_minutes",
"total_eve_calls",
"total_eve_charge",
"total_night_minutes",
"total_night_calls",
... | [
false,
false,
true,
false,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 3,326 |
362,907 | predictive_accuracy | accuracy_score | churn_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset churn (40701) 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 - state (numeric)],
1: [1 - account_length (numeric)],
2: [2 - area_code (nominal)],
3: [3 - phone_number (numeric)],
4: [4 - international_plan (nominal)],
5: [5 - voice_mail_plan (nominal)],
6: [6 - number_vmail_messages (numeric)],
7: [7 - total_day_minutes (numeric)],
8: [8 - total_day_calls (numeric... | {'MajorityClassSize': 1717.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 283.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 16.0,
'NumberOfSymbolicFeatures': 5.0,... | churn_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"state",
"account_length",
"area_code",
"phone_number",
"international_plan",
"voice_mail_plan",
"number_vmail_messages",
"total_day_minutes",
"total_day_calls",
"total_day_charge",
"total_eve_minutes",
"total_eve_calls",
"total_eve_charge",
"total_night_minutes",
"total_night_calls",
... | [
false,
false,
true,
false,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true
] | 3,327 |
362,759 | predictive_accuracy | accuracy_score | dilbert_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset dilbert (41163) 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 - V54 (numeric)],
1: [1 - V70 (numeric)],
2: [2 - V117 (numeric)],
3: [3 - V155 (numeric)],
4: [4 - V156 (numeric)],
5: [5 - V176 (numeric)],
6: [6 - V228 (numeric)],
7: [7 - V256 (numeric)],
8: [8 - V260 (numeric)],
9: [9 - V269 (numeric)],
10: [10 - V324 (numeric)],
11: [11 - V335 (numeric)],
12: [... | {'MajorityClassSize': 410.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 382.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0... | dilbert_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V54",
"V70",
"V117",
"V155",
"V156",
"V176",
"V228",
"V256",
"V260",
"V269",
"V324",
"V335",
"V341",
"V345",
"V357",
"V379",
"V394",
"V405",
"V422",
"V430",
"V439",
"V442",
"V528",
"V542",
"V590",
"V650",
"V686",
"V712",
"V715",
"V720",
"V721",
"V728",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,328 |
362,886 | predictive_accuracy | accuracy_score | dna_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset dna (40670) 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 = 10... | {0: [0 - A0 (nominal)],
1: [1 - A3 (nominal)],
2: [2 - A4 (nominal)],
3: [3 - A5 (nominal)],
4: [4 - A7 (nominal)],
5: [5 - A8 (nominal)],
6: [6 - A9 (nominal)],
7: [7 - A11 (nominal)],
8: [8 - A13 (nominal)],
9: [9 - A14 (nominal)],
10: [10 - A15 (nominal)],
11: [11 - A17 (nominal)],
12: [12 - A18 (nominal... | {'MajorityClassSize': 1038.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 480.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 101.... | dna_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"A0",
"A3",
"A4",
"A5",
"A7",
"A8",
"A9",
"A11",
"A13",
"A14",
"A15",
"A17",
"A18",
"A19",
"A25",
"A30",
"A31",
"A32",
"A34",
"A35",
"A36",
"A37",
"A39",
"A40",
"A44",
"A45",
"A51",
"A53",
"A56",
"A57",
"A62",
"A63",
"A65",
"A67",
"A68",
"A70",
... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true... | 3,329 |
362,806 | predictive_accuracy | accuracy_score | philippine_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset philippine (41145) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: i... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V8 (numeric)],
3: [3 - V9 (numeric)],
4: [4 - V12 (numeric)],
5: [5 - V18 (numeric)],
6: [6 - V20 (numeric)],
7: [7 - V21 (numeric)],
8: [8 - V25 (numeric)],
9: [9 - V26 (numeric)],
10: [10 - V32 (numeric)],
11: [11 - V36 (numeric)],
12: [12 - V38 (nume... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1... | philippine_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V8",
"V9",
"V12",
"V18",
"V20",
"V21",
"V25",
"V26",
"V32",
"V36",
"V38",
"V39",
"V41",
"V51",
"V58",
"V60",
"V63",
"V64",
"V67",
"V68",
"V69",
"V70",
"V73",
"V76",
"V78",
"V81",
"V84",
"V85",
"V90",
"V96",
"V98",
"V99",
"V101",
"V10... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,330 |
362,765 | predictive_accuracy | accuracy_score | dilbert_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset dilbert (41163) 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 - V3 (numeric)],
1: [1 - V10 (numeric)],
2: [2 - V60 (numeric)],
3: [3 - V63 (numeric)],
4: [4 - V76 (numeric)],
5: [5 - V85 (numeric)],
6: [6 - V150 (numeric)],
7: [7 - V163 (numeric)],
8: [8 - V179 (numeric)],
9: [9 - V180 (numeric)],
10: [10 - V205 (numeric)],
11: [11 - V219 (numeric)],
12: [12 - ... | {'MajorityClassSize': 410.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 382.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0... | dilbert_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V3",
"V10",
"V60",
"V63",
"V76",
"V85",
"V150",
"V163",
"V179",
"V180",
"V205",
"V219",
"V272",
"V307",
"V334",
"V342",
"V346",
"V373",
"V409",
"V434",
"V448",
"V451",
"V478",
"V490",
"V498",
"V499",
"V508",
"V550",
"V568",
"V579",
"V581",
"V589",
"V6... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,331 |
362,935 | predictive_accuracy | accuracy_score | wine-quality-white_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset wine-quality-white (40498) 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 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - Class (nominal)]} | {'MajorityClassSize': 898.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 2.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 1.0,
'... | wine-quality-white_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,332 |
362,921 | predictive_accuracy | accuracy_score | PhishingWebsites_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset PhishingWebsites (4534) 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_m... | {0: [0 - having_IP_Address (nominal)],
1: [1 - URL_Length (nominal)],
2: [2 - Shortining_Service (nominal)],
3: [3 - having_At_Symbol (nominal)],
4: [4 - double_slash_redirecting (nominal)],
5: [5 - Prefix_Suffix (nominal)],
6: [6 - having_Sub_Domain (nominal)],
7: [7 - SSLfinal_State (nominal)],
8: [8 - Domain... | {'MajorityClassSize': 1114.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 886.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 31.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 31.0,... | PhishingWebsites_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"having_IP_Address",
"URL_Length",
"Shortining_Service",
"having_At_Symbol",
"double_slash_redirecting",
"Prefix_Suffix",
"having_Sub_Domain",
"SSLfinal_State",
"Domain_registeration_length",
"Favicon",
"port",
"HTTPS_token",
"Request_URL",
"URL_of_Anchor",
"Links_in_tags",
"SFH",
"S... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true
] | 3,333 |
362,934 | predictive_accuracy | accuracy_score | wine-quality-white_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset wine-quality-white (40498) 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 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - Class (nominal)]} | {'MajorityClassSize': 898.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 2.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 1.0,
'... | wine-quality-white_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,334 |
362,924 | predictive_accuracy | accuracy_score | sylvine_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset sylvine (41146) with
seed=1
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int ... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 20.0,
'NumberOfSymbolicFeatures': 1.0... | sylvine_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,335 |
362,763 | predictive_accuracy | accuracy_score | dilbert_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset dilbert (41163) 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 - V78 (numeric)],
1: [1 - V106 (numeric)],
2: [2 - V114 (numeric)],
3: [3 - V152 (numeric)],
4: [4 - V176 (numeric)],
5: [5 - V193 (numeric)],
6: [6 - V202 (numeric)],
7: [7 - V205 (numeric)],
8: [8 - V209 (numeric)],
9: [9 - V210 (numeric)],
10: [10 - V289 (numeric)],
11: [11 - V360 (numeric)],
12: ... | {'MajorityClassSize': 410.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 382.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0... | dilbert_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V78",
"V106",
"V114",
"V152",
"V176",
"V193",
"V202",
"V205",
"V209",
"V210",
"V289",
"V360",
"V364",
"V387",
"V397",
"V422",
"V425",
"V432",
"V438",
"V498",
"V501",
"V517",
"V528",
"V569",
"V588",
"V621",
"V640",
"V647",
"V672",
"V676",
"V737",
"V759",... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,336 |
362,936 | predictive_accuracy | accuracy_score | wine-quality-white_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset wine-quality-white (40498) 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 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - Class (nominal)]} | {'MajorityClassSize': 898.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 2.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 1.0,
'... | wine-quality-white_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,337 |
362,897 | predictive_accuracy | accuracy_score | Internet-Advertisements_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Internet-Advertisements (40978) 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,
nc... | {0: [0 - aratio (numeric)],
1: [1 - url.hydrogeologist (nominal)],
2: [2 - url.www.FlowSoft.com (nominal)],
3: [3 - url.csuhayward.edu (nominal)],
4: [4 - url.romancebooks.pix (nominal)],
5: [5 - url.images.geoguideii (nominal)],
6: [6 - url.library.pitcairn (nominal)],
7: [7 - url.pawbutton.gif (nominal)],
8: ... | {'MajorityClassSize': 1720.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 280.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 1.0,
'NumberOfSymbolicFeatures': 100.... | Internet-Advertisements_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"aratio",
"url.hydrogeologist",
"url.www.FlowSoft.com",
"url.csuhayward.edu",
"url.romancebooks.pix",
"url.images.geoguideii",
"url.library.pitcairn",
"url.pawbutton.gif",
"url.geoguideii.pages",
"url.users.aol.com",
"url.www.martnet.com",
"url.polypkem",
"url.gifs",
"url.geoguideii.send",... | [
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
tru... | 3,338 |
362,895 | predictive_accuracy | accuracy_score | Internet-Advertisements_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Internet-Advertisements (40978) 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,
nc... | {0: [0 - url.ads.switchboard.com (nominal)],
1: [1 - url.keith.dumble (nominal)],
2: [2 - url.ucsd.edu (nominal)],
3: [3 - url.geoguideii.nh (nominal)],
4: [4 - url.derived (nominal)],
5: [5 - url.time (nominal)],
6: [6 - url.pharmacy.gif (nominal)],
7: [7 - url.forums (nominal)],
8: [8 - url.images.go2net.com ... | {'MajorityClassSize': 1720.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 280.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 101.... | Internet-Advertisements_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"url.ads.switchboard.com",
"url.keith.dumble",
"url.ucsd.edu",
"url.geoguideii.nh",
"url.derived",
"url.time",
"url.pharmacy.gif",
"url.forums",
"url.images.go2net.com",
"url.users.aol.com",
"url.www.cqi.com",
"url.claw1.gif",
"url.ball",
"url.htm.images",
"url.buttons",
"url.bull.gif"... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true... | 3,340 |
362,802 | predictive_accuracy | accuracy_score | philippine_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset philippine (41145) with
seed=0
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: i... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V6 (numeric)],
5: [5 - V8 (numeric)],
6: [6 - V9 (numeric)],
7: [7 - V15 (numeric)],
8: [8 - V17 (numeric)],
9: [9 - V20 (numeric)],
10: [10 - V21 (numeric)],
11: [11 - V22 (numeric)],
12: [12 - V26 (numeric... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1... | philippine_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V6",
"V8",
"V9",
"V15",
"V17",
"V20",
"V21",
"V22",
"V26",
"V32",
"V38",
"V39",
"V43",
"V58",
"V64",
"V66",
"V67",
"V69",
"V74",
"V75",
"V80",
"V87",
"V92",
"V94",
"V99",
"V100",
"V101",
"V104",
"V105",
"V106",
"V108",
"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... | 3,341 |
362,923 | predictive_accuracy | accuracy_score | sylvine_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset sylvine (41146) with
seed=0
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int ... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 20.0,
'NumberOfSymbolicFeatures': 1.0... | sylvine_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,342 |
362,933 | predictive_accuracy | accuracy_score | wine-quality-white_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset wine-quality-white (40498) 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 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - Class (nominal)]} | {'MajorityClassSize': 898.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 2.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 1.0,
'... | wine-quality-white_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,343 |
362,925 | predictive_accuracy | accuracy_score | sylvine_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset sylvine (41146) with
seed=2
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int ... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 20.0,
'NumberOfSymbolicFeatures': 1.0... | sylvine_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,344 |
362,927 | predictive_accuracy | accuracy_score | sylvine_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset sylvine (41146) 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 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 20.0,
'NumberOfSymbolicFeatures': 1.0... | sylvine_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,346 |
362,926 | predictive_accuracy | accuracy_score | sylvine_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset sylvine (41146) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: int ... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 20.0,
'NumberOfSymbolicFeatures': 1.0... | sylvine_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,347 |
362,937 | predictive_accuracy | accuracy_score | wine-quality-white_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset wine-quality-white (40498) 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,
nclasse... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - Class (nominal)]} | {'MajorityClassSize': 898.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 2.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 12.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 11.0,
'NumberOfSymbolicFeatures': 1.0,
'... | wine-quality-white_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,348 |
362,887 | predictive_accuracy | accuracy_score | dna_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset dna (40670) 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 = 10... | {0: [0 - A4 (nominal)],
1: [1 - A6 (nominal)],
2: [2 - A7 (nominal)],
3: [3 - A11 (nominal)],
4: [4 - A14 (nominal)],
5: [5 - A16 (nominal)],
6: [6 - A17 (nominal)],
7: [7 - A18 (nominal)],
8: [8 - A20 (nominal)],
9: [9 - A21 (nominal)],
10: [10 - A22 (nominal)],
11: [11 - A23 (nominal)],
12: [12 - A25 (nom... | {'MajorityClassSize': 1038.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 480.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 101.... | dna_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"A4",
"A6",
"A7",
"A11",
"A14",
"A16",
"A17",
"A18",
"A20",
"A21",
"A22",
"A23",
"A25",
"A27",
"A29",
"A31",
"A34",
"A36",
"A38",
"A40",
"A41",
"A42",
"A45",
"A48",
"A49",
"A50",
"A54",
"A55",
"A58",
"A61",
"A64",
"A65",
"A67",
"A68",
"A73",
"A74... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true... | 3,349 |
362,963 | predictive_accuracy | accuracy_score | jungle_chess_2pcs_raw_endgame_complete_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset jungle_chess_2pcs_raw_endgame_complete (41027) 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 = ... | {0: [0 - white_piece0_strength (numeric)],
1: [1 - white_piece0_file (numeric)],
2: [2 - white_piece0_rank (numeric)],
3: [3 - black_piece0_strength (numeric)],
4: [4 - black_piece0_file (numeric)],
5: [5 - black_piece0_rank (numeric)],
6: [6 - class (nominal)]} | {'MajorityClassSize': 1029.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 194.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 7.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 1.0,
... | jungle_chess_2pcs_raw_endgame_complete_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"white_piece0_strength",
"white_piece0_file",
"white_piece0_rank",
"black_piece0_strength",
"black_piece0_file",
"black_piece0_rank"
] | [
false,
false,
false,
false,
false,
false
] | 3,350 |
362,758 | predictive_accuracy | accuracy_score | dilbert_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset dilbert (41163) 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 - V39 (numeric)],
1: [1 - V53 (numeric)],
2: [2 - V67 (numeric)],
3: [3 - V79 (numeric)],
4: [4 - V109 (numeric)],
5: [5 - V121 (numeric)],
6: [6 - V124 (numeric)],
7: [7 - V165 (numeric)],
8: [8 - V180 (numeric)],
9: [9 - V228 (numeric)],
10: [10 - V240 (numeric)],
11: [11 - V241 (numeric)],
12: [12... | {'MajorityClassSize': 410.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 383.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0... | dilbert_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V39",
"V53",
"V67",
"V79",
"V109",
"V121",
"V124",
"V165",
"V180",
"V228",
"V240",
"V241",
"V260",
"V275",
"V296",
"V315",
"V395",
"V427",
"V476",
"V493",
"V509",
"V518",
"V523",
"V542",
"V546",
"V573",
"V576",
"V586",
"V596",
"V632",
"V636",
"V686",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,351 |
362,949 | predictive_accuracy | accuracy_score | connect-4_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset connect-4 (40668) 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 - a1 (nominal)],
1: [1 - a2 (nominal)],
2: [2 - a3 (nominal)],
3: [3 - a4 (nominal)],
4: [4 - a5 (nominal)],
5: [5 - a6 (nominal)],
6: [6 - b1 (nominal)],
7: [7 - b2 (nominal)],
8: [8 - b3 (nominal)],
9: [9 - b4 (nominal)],
10: [10 - b5 (nominal)],
11: [11 - b6 (nominal)],
12: [12 - c1 (nominal)],
1... | {'MajorityClassSize': 1317.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 191.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 43.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 43.0,... | connect-4_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"a1",
"a2",
"a3",
"a4",
"a5",
"a6",
"b1",
"b2",
"b3",
"b4",
"b5",
"b6",
"c1",
"c2",
"c3",
"c4",
"c5",
"c6",
"d1",
"d2",
"d3",
"d4",
"d5",
"d6",
"e1",
"e2",
"e3",
"e4",
"e5",
"e6",
"f1",
"f2",
"f3",
"f4",
"f5",
"f6",
"g1",
"g2",
"g3",
"g4"... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true... | 3,352 |
362,807 | predictive_accuracy | accuracy_score | philippine_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset philippine (41145) with
seed=4
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: i... | {0: [0 - V8 (numeric)],
1: [1 - V11 (numeric)],
2: [2 - V14 (numeric)],
3: [3 - V18 (numeric)],
4: [4 - V22 (numeric)],
5: [5 - V24 (numeric)],
6: [6 - V33 (numeric)],
7: [7 - V35 (numeric)],
8: [8 - V39 (numeric)],
9: [9 - V40 (numeric)],
10: [10 - V44 (numeric)],
11: [11 - V45 (numeric)],
12: [12 - V50 (n... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1... | philippine_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V8",
"V11",
"V14",
"V18",
"V22",
"V24",
"V33",
"V35",
"V39",
"V40",
"V44",
"V45",
"V50",
"V52",
"V53",
"V54",
"V57",
"V60",
"V63",
"V67",
"V75",
"V76",
"V78",
"V83",
"V89",
"V94",
"V96",
"V99",
"V101",
"V103",
"V106",
"V108",
"V109",
"V111",
"V115... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,353 |
362,804 | predictive_accuracy | accuracy_score | philippine_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset philippine (41145) with
seed=1
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: i... | {0: [0 - V5 (numeric)],
1: [1 - V7 (numeric)],
2: [2 - V8 (numeric)],
3: [3 - V12 (numeric)],
4: [4 - V16 (numeric)],
5: [5 - V17 (numeric)],
6: [6 - V18 (numeric)],
7: [7 - V20 (numeric)],
8: [8 - V24 (numeric)],
9: [9 - V30 (numeric)],
10: [10 - V31 (numeric)],
11: [11 - V32 (numeric)],
12: [12 - V33 (num... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1... | philippine_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V5",
"V7",
"V8",
"V12",
"V16",
"V17",
"V18",
"V20",
"V24",
"V30",
"V31",
"V32",
"V33",
"V43",
"V46",
"V51",
"V55",
"V58",
"V61",
"V63",
"V66",
"V68",
"V71",
"V73",
"V74",
"V77",
"V78",
"V80",
"V87",
"V92",
"V94",
"V99",
"V101",
"V103",
"V106",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,354 |
362,938 | predictive_accuracy | accuracy_score | Satellite_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Satellite (40900) 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 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - V5 (numeric)],
5: [5 - V6 (numeric)],
6: [6 - V7 (numeric)],
7: [7 - V8 (numeric)],
8: [8 - V9 (numeric)],
9: [9 - V10 (numeric)],
10: [10 - V11 (numeric)],
11: [11 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 1971.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 29.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 37.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 36.0,
'NumberOfSymbolicFeatures': 1.0,
... | Satellite_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27",
"V28",
"V29",
"V30",
"V31",
"V32",
"V33",
"V34",
"V35",
"V36"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 3,355 |
362,896 | predictive_accuracy | accuracy_score | Internet-Advertisements_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Internet-Advertisements (40978) 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,
nc... | {0: [0 - url.pool.images (nominal)],
1: [1 - url.infoserver.etl.vt.edu (nominal)],
2: [2 - url.charlie (nominal)],
3: [3 - url.derived (nominal)],
4: [4 - url.home (nominal)],
5: [5 - url.www.ran.org (nominal)],
6: [6 - url.sjsu.edu (nominal)],
7: [7 - url.gra (nominal)],
8: [8 - url.www.express.scripts.com (no... | {'MajorityClassSize': 1720.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 280.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 101.... | Internet-Advertisements_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"url.pool.images",
"url.infoserver.etl.vt.edu",
"url.charlie",
"url.derived",
"url.home",
"url.www.ran.org",
"url.sjsu.edu",
"url.gra",
"url.www.express.scripts.com",
"url.www.finest.tm.fr",
"url.users",
"url.geoguideii.send",
"url.w.gif",
"url.aol.com",
"url.ball",
"url.logo.b",
"ur... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true... | 3,356 |
362,948 | predictive_accuracy | accuracy_score | connect-4_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset connect-4 (40668) 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 - a1 (nominal)],
1: [1 - a2 (nominal)],
2: [2 - a3 (nominal)],
3: [3 - a4 (nominal)],
4: [4 - a5 (nominal)],
5: [5 - a6 (nominal)],
6: [6 - b1 (nominal)],
7: [7 - b2 (nominal)],
8: [8 - b3 (nominal)],
9: [9 - b4 (nominal)],
10: [10 - b5 (nominal)],
11: [11 - b6 (nominal)],
12: [12 - c1 (nominal)],
1... | {'MajorityClassSize': 1317.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 191.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 43.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 43.0,... | connect-4_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"a1",
"a2",
"a3",
"a4",
"a5",
"a6",
"b1",
"b2",
"b3",
"b4",
"b5",
"b6",
"c1",
"c2",
"c3",
"c4",
"c5",
"c6",
"d1",
"d2",
"d3",
"d4",
"d5",
"d6",
"e1",
"e2",
"e3",
"e4",
"e5",
"e6",
"f1",
"f2",
"f3",
"f4",
"f5",
"f6",
"g1",
"g2",
"g3",
"g4"... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true... | 3,357 |
362,951 | predictive_accuracy | accuracy_score | connect-4_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset connect-4 (40668) 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 - a1 (nominal)],
1: [1 - a2 (nominal)],
2: [2 - a3 (nominal)],
3: [3 - a4 (nominal)],
4: [4 - a5 (nominal)],
5: [5 - a6 (nominal)],
6: [6 - b1 (nominal)],
7: [7 - b2 (nominal)],
8: [8 - b3 (nominal)],
9: [9 - b4 (nominal)],
10: [10 - b5 (nominal)],
11: [11 - b6 (nominal)],
12: [12 - c1 (nominal)],
1... | {'MajorityClassSize': 1317.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 191.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 43.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 43.0,... | connect-4_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"a1",
"a2",
"a3",
"a4",
"a5",
"a6",
"b1",
"b2",
"b3",
"b4",
"b5",
"b6",
"c1",
"c2",
"c3",
"c4",
"c5",
"c6",
"d1",
"d2",
"d3",
"d4",
"d5",
"d6",
"e1",
"e2",
"e3",
"e4",
"e5",
"e6",
"f1",
"f2",
"f3",
"f4",
"f5",
"f6",
"g1",
"g2",
"g3",
"g4"... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true... | 3,358 |
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