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 |
|---|---|---|---|---|---|---|---|---|---|---|
362,661 | predictive_accuracy | accuracy_score | MiniBooNE_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset MiniBooNE (44128) 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 - ParticleID_0 (numeric)],
1: [1 - ParticleID_1 (numeric)],
2: [2 - ParticleID_2 (numeric)],
3: [3 - ParticleID_3 (numeric)],
4: [4 - ParticleID_4 (numeric)],
5: [5 - ParticleID_5 (numeric)],
6: [6 - ParticleID_6 (numeric)],
7: [7 - ParticleID_7 (numeric)],
8: [8 - ParticleID_8 (numeric)],
9: [9 - Parti... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0... | MiniBooNE_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"ParticleID_0",
"ParticleID_1",
"ParticleID_2",
"ParticleID_3",
"ParticleID_4",
"ParticleID_5",
"ParticleID_6",
"ParticleID_7",
"ParticleID_8",
"ParticleID_9",
"ParticleID_10",
"ParticleID_11",
"ParticleID_12",
"ParticleID_13",
"ParticleID_14",
"ParticleID_15",
"ParticleID_16",
"Pa... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,148 |
362,746 | predictive_accuracy | accuracy_score | credit-g_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset credit-g (31) 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 - checking_status (nominal)],
1: [1 - duration (numeric)],
2: [2 - credit_history (nominal)],
3: [3 - purpose (nominal)],
4: [4 - credit_amount (numeric)],
5: [5 - savings_status (nominal)],
6: [6 - employment (nominal)],
7: [7 - installment_commitment (numeric)],
8: [8 - personal_status (nominal)],
9: ... | {'MajorityClassSize': 700.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 300.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 14.0,
... | credit-g_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"checking_status",
"duration",
"credit_history",
"purpose",
"credit_amount",
"savings_status",
"employment",
"installment_commitment",
"personal_status",
"other_parties",
"residence_since",
"property_magnitude",
"age",
"other_payment_plans",
"housing",
"existing_credits",
"job",
"n... | [
true,
false,
true,
true,
false,
true,
true,
false,
true,
true,
false,
true,
false,
true,
true,
false,
true,
false,
true,
true
] | 3,149 |
362,748 | predictive_accuracy | accuracy_score | credit-g_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset credit-g (31) 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 - checking_status (nominal)],
1: [1 - duration (numeric)],
2: [2 - credit_history (nominal)],
3: [3 - purpose (nominal)],
4: [4 - credit_amount (numeric)],
5: [5 - savings_status (nominal)],
6: [6 - employment (nominal)],
7: [7 - installment_commitment (numeric)],
8: [8 - personal_status (nominal)],
9: ... | {'MajorityClassSize': 700.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 300.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 21.0,
'NumberOfInstances': 1000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 7.0,
'NumberOfSymbolicFeatures': 14.0,
... | credit-g_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"checking_status",
"duration",
"credit_history",
"purpose",
"credit_amount",
"savings_status",
"employment",
"installment_commitment",
"personal_status",
"other_parties",
"residence_since",
"property_magnitude",
"age",
"other_payment_plans",
"housing",
"existing_credits",
"job",
"n... | [
true,
false,
true,
true,
false,
true,
true,
false,
true,
true,
false,
true,
false,
true,
true,
false,
true,
false,
true,
true
] | 3,150 |
362,721 | predictive_accuracy | accuracy_score | amazon-commerce-reviews_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset amazon-commerce-reviews (1457) with
seed=0
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
ncl... | {0: [0 - V28 (numeric)],
1: [1 - V54 (numeric)],
2: [2 - V83 (numeric)],
3: [3 - V220 (numeric)],
4: [4 - V281 (numeric)],
5: [5 - V333 (numeric)],
6: [6 - V485 (numeric)],
7: [7 - V521 (numeric)],
8: [8 - V585 (numeric)],
9: [9 - V727 (numeric)],
10: [10 - V792 (numeric)],
11: [11 - V798 (numeric)],
12: [1... | {'MajorityClassSize': 30.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 30.0,
'NumberOfClasses': 50.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 1500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0,... | amazon-commerce-reviews_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V28",
"V54",
"V83",
"V220",
"V281",
"V333",
"V485",
"V521",
"V585",
"V727",
"V792",
"V798",
"V839",
"V886",
"V1234",
"V1344",
"V1502",
"V1742",
"V2266",
"V2306",
"V2390",
"V2537",
"V2555",
"V2633",
"V2747",
"V2973",
"V3088",
"V3160",
"V3208",
"V3273",
"V3... | [
false,
false,
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false,
false,
false,
false,
false,
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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... | 3,151 |
362,392 | predictive_accuracy | accuracy_score | mini_insect_1 | mini insect example dataset # 1 | {0: [0 - Data (string)],
1: [1 - Shape (nominal)],
2: [2 - Label (nominal)],
3: [3 - SuperCategory (nominal)]} | {'MajorityClassSize': 3.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 3.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 12.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 3.0,
'cost_m... | mini_insect_1 | [
"Data",
"Shape",
"SuperCategory"
] | [
false,
true,
true
] | 3,152 |
362,657 | predictive_accuracy | accuracy_score | MiniBooNE_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset MiniBooNE (44128) 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 - ParticleID_0 (numeric)],
1: [1 - ParticleID_1 (numeric)],
2: [2 - ParticleID_2 (numeric)],
3: [3 - ParticleID_3 (numeric)],
4: [4 - ParticleID_4 (numeric)],
5: [5 - ParticleID_5 (numeric)],
6: [6 - ParticleID_6 (numeric)],
7: [7 - ParticleID_7 (numeric)],
8: [8 - ParticleID_8 (numeric)],
9: [9 - Parti... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0... | MiniBooNE_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"ParticleID_0",
"ParticleID_1",
"ParticleID_2",
"ParticleID_3",
"ParticleID_4",
"ParticleID_5",
"ParticleID_6",
"ParticleID_7",
"ParticleID_8",
"ParticleID_9",
"ParticleID_10",
"ParticleID_11",
"ParticleID_12",
"ParticleID_13",
"ParticleID_14",
"ParticleID_15",
"ParticleID_16",
"Pa... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,153 |
362,659 | predictive_accuracy | accuracy_score | MiniBooNE_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset MiniBooNE (44128) 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 - ParticleID_0 (numeric)],
1: [1 - ParticleID_1 (numeric)],
2: [2 - ParticleID_2 (numeric)],
3: [3 - ParticleID_3 (numeric)],
4: [4 - ParticleID_4 (numeric)],
5: [5 - ParticleID_5 (numeric)],
6: [6 - ParticleID_6 (numeric)],
7: [7 - ParticleID_7 (numeric)],
8: [8 - ParticleID_8 (numeric)],
9: [9 - Parti... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0... | MiniBooNE_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"ParticleID_0",
"ParticleID_1",
"ParticleID_2",
"ParticleID_3",
"ParticleID_4",
"ParticleID_5",
"ParticleID_6",
"ParticleID_7",
"ParticleID_8",
"ParticleID_9",
"ParticleID_10",
"ParticleID_11",
"ParticleID_12",
"ParticleID_13",
"ParticleID_14",
"ParticleID_15",
"ParticleID_16",
"Pa... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,154 |
362,709 | predictive_accuracy | accuracy_score | KDDCup09_upselling_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset KDDCup09_upselling (44186) 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 - Var6 (numeric)],
1: [1 - Var13 (numeric)],
2: [2 - Var21 (numeric)],
3: [3 - Var22 (numeric)],
4: [4 - Var24 (numeric)],
5: [5 - Var25 (numeric)],
6: [6 - Var28 (numeric)],
7: [7 - Var35 (numeric)],
8: [8 - Var38 (numeric)],
9: [9 - Var57 (numeric)],
10: [10 - Var65 (numeric)],
11: [11 - Var73 (nume... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 50.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 34.0,
'NumberOfSymbolicFeatures': 16.... | KDDCup09_upselling_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Var6",
"Var13",
"Var21",
"Var22",
"Var24",
"Var25",
"Var28",
"Var35",
"Var38",
"Var57",
"Var65",
"Var73",
"Var74",
"Var76",
"Var78",
"Var81",
"Var83",
"Var85",
"Var109",
"Var112",
"Var113",
"Var119",
"Var123",
"Var125",
"Var126",
"Var132",
"Var133",
"Var134",
... | [
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,
true,
tr... | 3,155 |
362,712 | predictive_accuracy | accuracy_score | KDDCup09_upselling_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset KDDCup09_upselling (44186) 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 - Var6 (numeric)],
1: [1 - Var13 (numeric)],
2: [2 - Var21 (numeric)],
3: [3 - Var22 (numeric)],
4: [4 - Var24 (numeric)],
5: [5 - Var25 (numeric)],
6: [6 - Var28 (numeric)],
7: [7 - Var35 (numeric)],
8: [8 - Var38 (numeric)],
9: [9 - Var57 (numeric)],
10: [10 - Var65 (numeric)],
11: [11 - Var73 (nume... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 50.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 34.0,
'NumberOfSymbolicFeatures': 16.... | KDDCup09_upselling_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Var6",
"Var13",
"Var21",
"Var22",
"Var24",
"Var25",
"Var28",
"Var35",
"Var38",
"Var57",
"Var65",
"Var73",
"Var74",
"Var76",
"Var78",
"Var81",
"Var83",
"Var85",
"Var109",
"Var112",
"Var113",
"Var119",
"Var123",
"Var125",
"Var126",
"Var132",
"Var133",
"Var134",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true,
tr... | 3,156 |
4,632 | predictive_accuracy | accuracy_score | rsctc2010_4 | **Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Data from the RSCTC 2010 Discovery Challenge. Example datasets for 6 different problems of DNA microarray data analysis and classification. All datasets contain gene expression data characterized by values of 20,000 - 65,000 attributes. Samples ar... | {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': 51.0,
'MaxNominalAttDistinctValues': 5.0,
'MinorityClassSize': 10.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 54676.0,
'NumberOfInstances': 113.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 54675.0,
'NumberOfSymbolicFeatures': 1.... | rsctc2010_4 | [
"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... | [
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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,
f... | 3,157 |
362,674 | predictive_accuracy | accuracy_score | jannis_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset jannis (44131) 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': 55.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 54.0,
'NumberOfSymbolicFeatures': 1.0... | jannis_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,
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false,
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f... | 3,158 |
362,658 | predictive_accuracy | accuracy_score | MiniBooNE_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset MiniBooNE (44128) 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 - ParticleID_0 (numeric)],
1: [1 - ParticleID_1 (numeric)],
2: [2 - ParticleID_2 (numeric)],
3: [3 - ParticleID_3 (numeric)],
4: [4 - ParticleID_4 (numeric)],
5: [5 - ParticleID_5 (numeric)],
6: [6 - ParticleID_6 (numeric)],
7: [7 - ParticleID_7 (numeric)],
8: [8 - ParticleID_8 (numeric)],
9: [9 - Parti... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0... | MiniBooNE_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"ParticleID_0",
"ParticleID_1",
"ParticleID_2",
"ParticleID_3",
"ParticleID_4",
"ParticleID_5",
"ParticleID_6",
"ParticleID_7",
"ParticleID_8",
"ParticleID_9",
"ParticleID_10",
"ParticleID_11",
"ParticleID_12",
"ParticleID_13",
"ParticleID_14",
"ParticleID_15",
"ParticleID_16",
"Pa... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,159 |
362,672 | predictive_accuracy | accuracy_score | jannis_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset jannis (44131) 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': 55.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 54.0,
'NumberOfSymbolicFeatures': 1.0... | jannis_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... | 3,160 |
362,722 | predictive_accuracy | accuracy_score | amazon-commerce-reviews_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset amazon-commerce-reviews (1457) 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,
ncl... | {0: [0 - V386 (numeric)],
1: [1 - V395 (numeric)],
2: [2 - V568 (numeric)],
3: [3 - V763 (numeric)],
4: [4 - V965 (numeric)],
5: [5 - V1005 (numeric)],
6: [6 - V1039 (numeric)],
7: [7 - V1066 (numeric)],
8: [8 - V1068 (numeric)],
9: [9 - V1486 (numeric)],
10: [10 - V1718 (numeric)],
11: [11 - V1858 (numeric)... | {'MajorityClassSize': 30.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 30.0,
'NumberOfClasses': 50.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 1500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0,... | amazon-commerce-reviews_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V386",
"V395",
"V568",
"V763",
"V965",
"V1005",
"V1039",
"V1066",
"V1068",
"V1486",
"V1718",
"V1858",
"V1962",
"V2007",
"V2122",
"V2157",
"V2178",
"V2223",
"V2576",
"V2582",
"V2672",
"V3159",
"V3214",
"V3303",
"V3381",
"V3432",
"V3629",
"V3770",
"V3847",
"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... | 3,161 |
362,710 | predictive_accuracy | accuracy_score | KDDCup09_upselling_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset KDDCup09_upselling (44186) 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 - Var6 (numeric)],
1: [1 - Var13 (numeric)],
2: [2 - Var21 (numeric)],
3: [3 - Var22 (numeric)],
4: [4 - Var24 (numeric)],
5: [5 - Var25 (numeric)],
6: [6 - Var28 (numeric)],
7: [7 - Var35 (numeric)],
8: [8 - Var38 (numeric)],
9: [9 - Var57 (numeric)],
10: [10 - Var65 (numeric)],
11: [11 - Var73 (nume... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 50.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 34.0,
'NumberOfSymbolicFeatures': 16.... | KDDCup09_upselling_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Var6",
"Var13",
"Var21",
"Var22",
"Var24",
"Var25",
"Var28",
"Var35",
"Var38",
"Var57",
"Var65",
"Var73",
"Var74",
"Var76",
"Var78",
"Var81",
"Var83",
"Var85",
"Var109",
"Var112",
"Var113",
"Var119",
"Var123",
"Var125",
"Var126",
"Var132",
"Var133",
"Var134",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true,
tr... | 3,162 |
362,711 | predictive_accuracy | accuracy_score | KDDCup09_upselling_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset KDDCup09_upselling (44186) 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 - Var6 (numeric)],
1: [1 - Var13 (numeric)],
2: [2 - Var21 (numeric)],
3: [3 - Var22 (numeric)],
4: [4 - Var24 (numeric)],
5: [5 - Var25 (numeric)],
6: [6 - Var28 (numeric)],
7: [7 - Var35 (numeric)],
8: [8 - Var38 (numeric)],
9: [9 - Var57 (numeric)],
10: [10 - Var65 (numeric)],
11: [11 - Var73 (nume... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 50.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 34.0,
'NumberOfSymbolicFeatures': 16.... | KDDCup09_upselling_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Var6",
"Var13",
"Var21",
"Var22",
"Var24",
"Var25",
"Var28",
"Var35",
"Var38",
"Var57",
"Var65",
"Var73",
"Var74",
"Var76",
"Var78",
"Var81",
"Var83",
"Var85",
"Var109",
"Var112",
"Var113",
"Var119",
"Var123",
"Var125",
"Var126",
"Var132",
"Var133",
"Var134",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
true,
tr... | 3,163 |
362,400 | predictive_accuracy | accuracy_score | mini_insect_1 | ### This is a dataset with dummy description | {0: [0 - Data (string)],
1: [1 - Shape (nominal)],
2: [2 - Label (nominal)],
3: [3 - SuperCategory (nominal)]} | {'MajorityClassSize': 3.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 3.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 12.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 3.0,
'cost_m... | mini_insect_1 | [
"Data",
"Shape",
"SuperCategory"
] | [
false,
true,
true
] | 3,164 |
362,403 | predictive_accuracy | accuracy_score | mini_insect_1 | ### Description mini_insect_1 | {0: [0 - Data (string)],
1: [1 - Shape (nominal)],
2: [2 - CATEGORY (nominal)],
3: [3 - SUPER_CATEGORY (nominal)]} | {'MajorityClassSize': 3.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 3.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 12.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 3.0,
'cost_m... | mini_insect_1 | [
"Data",
"Shape",
"SUPER_CATEGORY"
] | [
false,
true,
true
] | 3,165 |
362,772 | predictive_accuracy | accuracy_score | airlines_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset airlines (1169) 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 - Airline (nominal)],
1: [1 - Flight (numeric)],
2: [2 - AirportFrom (nominal)],
3: [3 - AirportTo (nominal)],
4: [4 - DayOfWeek (nominal)],
5: [5 - Time (numeric)],
6: [6 - Length (numeric)],
7: [7 - Delay (nominal)]} | {'MajorityClassSize': 1109.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 891.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 5.0,
... | airlines_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Airline",
"Flight",
"AirportFrom",
"AirportTo",
"DayOfWeek",
"Time",
"Length"
] | [
true,
false,
true,
true,
true,
false,
false
] | 3,166 |
362,777 | predictive_accuracy | accuracy_score | airlines_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset airlines (1169) 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 - Airline (nominal)],
1: [1 - Flight (numeric)],
2: [2 - AirportFrom (nominal)],
3: [3 - AirportTo (nominal)],
4: [4 - DayOfWeek (nominal)],
5: [5 - Time (numeric)],
6: [6 - Length (numeric)],
7: [7 - Delay (nominal)]} | {'MajorityClassSize': 1109.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 891.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 5.0,
... | airlines_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Airline",
"Flight",
"AirportFrom",
"AirportTo",
"DayOfWeek",
"Time",
"Length"
] | [
true,
false,
true,
true,
true,
false,
false
] | 3,167 |
362,749 | predictive_accuracy | accuracy_score | steel-plates-fault_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset steel-plates-fault (40982) 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 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 673.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 55.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 28.0,
'NumberOfInstances': 1941.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 27.0,
'NumberOfSymbolicFeatures': 1.0,
... | steel-plates-fault_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,168 |
362,774 | predictive_accuracy | accuracy_score | airlines_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset airlines (1169) 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 - Airline (nominal)],
1: [1 - Flight (numeric)],
2: [2 - AirportFrom (nominal)],
3: [3 - AirportTo (nominal)],
4: [4 - DayOfWeek (nominal)],
5: [5 - Time (numeric)],
6: [6 - Length (numeric)],
7: [7 - Delay (nominal)]} | {'MajorityClassSize': 1109.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 891.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 5.0,
... | airlines_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Airline",
"Flight",
"AirportFrom",
"AirportTo",
"DayOfWeek",
"Time",
"Length"
] | [
true,
false,
true,
true,
true,
false,
false
] | 3,169 |
362,676 | predictive_accuracy | accuracy_score | jannis_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset jannis (44131) 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': 55.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 54.0,
'NumberOfSymbolicFeatures': 1.0... | jannis_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27",
"V28",
"V29",
"V30",
"V31",
"V32",
"V33",
"V34",
"V35",
"V36",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 3,170 |
362,739 | predictive_accuracy | accuracy_score | numerai28.6_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset numerai28.6 (23517) with
seed=0
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: ... | {0: [0 - attribute_0 (numeric)],
1: [1 - attribute_1 (numeric)],
2: [2 - attribute_2 (numeric)],
3: [3 - attribute_3 (numeric)],
4: [4 - attribute_4 (numeric)],
5: [5 - attribute_5 (numeric)],
6: [6 - attribute_6 (numeric)],
7: [7 - attribute_7 (numeric)],
8: [8 - attribute_8 (numeric)],
9: [9 - attribute_9 (n... | {'MajorityClassSize': 1010.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 990.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 22.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 21.0,
'NumberOfSymbolicFeatures': 1.0,... | numerai28.6_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"attribute_0",
"attribute_1",
"attribute_2",
"attribute_3",
"attribute_4",
"attribute_5",
"attribute_6",
"attribute_7",
"attribute_8",
"attribute_9",
"attribute_10",
"attribute_11",
"attribute_12",
"attribute_13",
"attribute_14",
"attribute_15",
"attribute_16",
"attribute_17",
"a... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,171 |
362,776 | predictive_accuracy | accuracy_score | airlines_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset airlines (1169) 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 - Airline (nominal)],
1: [1 - Flight (numeric)],
2: [2 - AirportFrom (nominal)],
3: [3 - AirportTo (nominal)],
4: [4 - DayOfWeek (nominal)],
5: [5 - Time (numeric)],
6: [6 - Length (numeric)],
7: [7 - Delay (nominal)]} | {'MajorityClassSize': 1109.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 891.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 5.0,
... | airlines_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Airline",
"Flight",
"AirportFrom",
"AirportTo",
"DayOfWeek",
"Time",
"Length"
] | [
true,
false,
true,
true,
true,
false,
false
] | 3,172 |
362,726 | predictive_accuracy | accuracy_score | amazon-commerce-reviews_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset amazon-commerce-reviews (1457) 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,
ncl... | {0: [0 - V268 (numeric)],
1: [1 - V347 (numeric)],
2: [2 - V420 (numeric)],
3: [3 - V600 (numeric)],
4: [4 - V713 (numeric)],
5: [5 - V788 (numeric)],
6: [6 - V896 (numeric)],
7: [7 - V972 (numeric)],
8: [8 - V1149 (numeric)],
9: [9 - V1276 (numeric)],
10: [10 - V1325 (numeric)],
11: [11 - V1382 (numeric)],
... | {'MajorityClassSize': 30.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 30.0,
'NumberOfClasses': 50.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 1500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0,... | amazon-commerce-reviews_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V268",
"V347",
"V420",
"V600",
"V713",
"V788",
"V896",
"V972",
"V1149",
"V1276",
"V1325",
"V1382",
"V1530",
"V1639",
"V1703",
"V1706",
"V1763",
"V1791",
"V1902",
"V1997",
"V2062",
"V2162",
"V2208",
"V2225",
"V2665",
"V2758",
"V2954",
"V3016",
"V3044",
"V328... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,173 |
362,677 | predictive_accuracy | accuracy_score | jannis_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset jannis (44131) 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': 55.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 54.0,
'NumberOfSymbolicFeatures': 1.0... | jannis_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... | 3,174 |
362,750 | predictive_accuracy | accuracy_score | steel-plates-fault_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset steel-plates-fault (40982) 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 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 673.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 55.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 28.0,
'NumberOfInstances': 1941.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 27.0,
'NumberOfSymbolicFeatures': 1.0,
... | steel-plates-fault_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,175 |
362,723 | predictive_accuracy | accuracy_score | amazon-commerce-reviews_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset amazon-commerce-reviews (1457) with
seed=1
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
ncl... | {0: [0 - V197 (numeric)],
1: [1 - V395 (numeric)],
2: [2 - V540 (numeric)],
3: [3 - V616 (numeric)],
4: [4 - V621 (numeric)],
5: [5 - V689 (numeric)],
6: [6 - V815 (numeric)],
7: [7 - V915 (numeric)],
8: [8 - V1152 (numeric)],
9: [9 - V1161 (numeric)],
10: [10 - V1228 (numeric)],
11: [11 - V1233 (numeric)],
... | {'MajorityClassSize': 30.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 30.0,
'NumberOfClasses': 50.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 1500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0,... | amazon-commerce-reviews_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V197",
"V395",
"V540",
"V616",
"V621",
"V689",
"V815",
"V915",
"V1152",
"V1161",
"V1228",
"V1233",
"V1329",
"V1476",
"V1597",
"V1911",
"V1914",
"V2018",
"V2141",
"V2199",
"V2593",
"V2602",
"V2741",
"V2751",
"V2783",
"V2912",
"V2913",
"V3005",
"V3105",
"V320... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,176 |
362,747 | predictive_accuracy | accuracy_score | steel-plates-fault_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset steel-plates-fault (40982) 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 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 673.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 55.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 28.0,
'NumberOfInstances': 1941.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 27.0,
'NumberOfSymbolicFeatures': 1.0,
... | steel-plates-fault_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27"
] | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,177 |
362,775 | predictive_accuracy | accuracy_score | airlines_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset airlines (1169) 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 - Airline (nominal)],
1: [1 - Flight (numeric)],
2: [2 - AirportFrom (nominal)],
3: [3 - AirportTo (nominal)],
4: [4 - DayOfWeek (nominal)],
5: [5 - Time (numeric)],
6: [6 - Length (numeric)],
7: [7 - Delay (nominal)]} | {'MajorityClassSize': 1109.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 891.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 8.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 3.0,
'NumberOfSymbolicFeatures': 5.0,
... | airlines_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Airline",
"Flight",
"AirportFrom",
"AirportTo",
"DayOfWeek",
"Time",
"Length"
] | [
true,
false,
true,
true,
true,
false,
false
] | 3,178 |
362,724 | predictive_accuracy | accuracy_score | amazon-commerce-reviews_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset amazon-commerce-reviews (1457) 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,
ncl... | {0: [0 - V15 (numeric)],
1: [1 - V48 (numeric)],
2: [2 - V302 (numeric)],
3: [3 - V428 (numeric)],
4: [4 - V764 (numeric)],
5: [5 - V904 (numeric)],
6: [6 - V1021 (numeric)],
7: [7 - V1289 (numeric)],
8: [8 - V1383 (numeric)],
9: [9 - V1461 (numeric)],
10: [10 - V1483 (numeric)],
11: [11 - V1713 (numeric)],
... | {'MajorityClassSize': 30.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 30.0,
'NumberOfClasses': 50.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 1500.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0,... | amazon-commerce-reviews_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V15",
"V48",
"V302",
"V428",
"V764",
"V904",
"V1021",
"V1289",
"V1383",
"V1461",
"V1483",
"V1713",
"V1803",
"V1891",
"V1966",
"V2062",
"V2123",
"V2178",
"V2244",
"V2430",
"V2468",
"V2522",
"V2529",
"V2751",
"V2818",
"V2904",
"V2912",
"V2916",
"V2962",
"V296... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,179 |
362,660 | predictive_accuracy | accuracy_score | MiniBooNE_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset MiniBooNE (44128) 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 - ParticleID_0 (numeric)],
1: [1 - ParticleID_1 (numeric)],
2: [2 - ParticleID_2 (numeric)],
3: [3 - ParticleID_3 (numeric)],
4: [4 - ParticleID_4 (numeric)],
5: [5 - ParticleID_5 (numeric)],
6: [6 - ParticleID_6 (numeric)],
7: [7 - ParticleID_7 (numeric)],
8: [8 - ParticleID_8 (numeric)],
9: [9 - Parti... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 51.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 50.0,
'NumberOfSymbolicFeatures': 1.0... | MiniBooNE_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"ParticleID_0",
"ParticleID_1",
"ParticleID_2",
"ParticleID_3",
"ParticleID_4",
"ParticleID_5",
"ParticleID_6",
"ParticleID_7",
"ParticleID_8",
"ParticleID_9",
"ParticleID_10",
"ParticleID_11",
"ParticleID_12",
"ParticleID_13",
"ParticleID_14",
"ParticleID_15",
"ParticleID_16",
"Pa... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,181 |
362,797 | predictive_accuracy | accuracy_score | vehicle_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset vehicle (54) 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 - COMPACTNESS (numeric)],
1: [1 - CIRCULARITY (numeric)],
2: [2 - DISTANCE_CIRCULARITY (numeric)],
3: [3 - RADIUS_RATIO (numeric)],
4: [4 - PR.AXIS_ASPECT_RATIO (numeric)],
5: [5 - MAX.LENGTH_ASPECT_RATIO (numeric)],
6: [6 - SCATTER_RATIO (numeric)],
7: [7 - ELONGATEDNESS (numeric)],
8: [8 - PR.AXIS_RECT... | {'MajorityClassSize': 218.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 199.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 19.0,
'NumberOfInstances': 846.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 18.0,
'NumberOfSymbolicFeatures': 1.0,
... | vehicle_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"COMPACTNESS",
"CIRCULARITY",
"DISTANCE_CIRCULARITY",
"RADIUS_RATIO",
"PR.AXIS_ASPECT_RATIO",
"MAX.LENGTH_ASPECT_RATIO",
"SCATTER_RATIO",
"ELONGATEDNESS",
"PR.AXIS_RECTANGULARITY",
"MAX.LENGTH_RECTANGULARITY",
"SCALED_VARIANCE_MAJOR",
"SCALED_VARIANCE_MINOR",
"SCALED_RADIUS_OF_GYRATION",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,182 |
362,795 | predictive_accuracy | accuracy_score | vehicle_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset vehicle (54) 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 - COMPACTNESS (numeric)],
1: [1 - CIRCULARITY (numeric)],
2: [2 - DISTANCE_CIRCULARITY (numeric)],
3: [3 - RADIUS_RATIO (numeric)],
4: [4 - PR.AXIS_ASPECT_RATIO (numeric)],
5: [5 - MAX.LENGTH_ASPECT_RATIO (numeric)],
6: [6 - SCATTER_RATIO (numeric)],
7: [7 - ELONGATEDNESS (numeric)],
8: [8 - PR.AXIS_RECT... | {'MajorityClassSize': 218.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 199.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 19.0,
'NumberOfInstances': 846.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 18.0,
'NumberOfSymbolicFeatures': 1.0,
... | vehicle_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"COMPACTNESS",
"CIRCULARITY",
"DISTANCE_CIRCULARITY",
"RADIUS_RATIO",
"PR.AXIS_ASPECT_RATIO",
"MAX.LENGTH_ASPECT_RATIO",
"SCATTER_RATIO",
"ELONGATEDNESS",
"PR.AXIS_RECTANGULARITY",
"MAX.LENGTH_RECTANGULARITY",
"SCALED_VARIANCE_MAJOR",
"SCALED_VARIANCE_MINOR",
"SCALED_RADIUS_OF_GYRATION",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,183 |
362,752 | predictive_accuracy | accuracy_score | steel-plates-fault_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset steel-plates-fault (40982) 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 - V12 (numeric)],
12: [12 - V13 (numeric)]... | {'MajorityClassSize': 673.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 55.0,
'NumberOfClasses': 7.0,
'NumberOfFeatures': 28.0,
'NumberOfInstances': 1941.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 27.0,
'NumberOfSymbolicFeatures': 1.0,
... | steel-plates-fault_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"
] | [
false,
false,
false,
false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,184 |
362,794 | predictive_accuracy | accuracy_score | vehicle_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset vehicle (54) 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 - COMPACTNESS (numeric)],
1: [1 - CIRCULARITY (numeric)],
2: [2 - DISTANCE_CIRCULARITY (numeric)],
3: [3 - RADIUS_RATIO (numeric)],
4: [4 - PR.AXIS_ASPECT_RATIO (numeric)],
5: [5 - MAX.LENGTH_ASPECT_RATIO (numeric)],
6: [6 - SCATTER_RATIO (numeric)],
7: [7 - ELONGATEDNESS (numeric)],
8: [8 - PR.AXIS_RECT... | {'MajorityClassSize': 218.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 199.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 19.0,
'NumberOfInstances': 846.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 18.0,
'NumberOfSymbolicFeatures': 1.0,
... | vehicle_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"COMPACTNESS",
"CIRCULARITY",
"DISTANCE_CIRCULARITY",
"RADIUS_RATIO",
"PR.AXIS_ASPECT_RATIO",
"MAX.LENGTH_ASPECT_RATIO",
"SCATTER_RATIO",
"ELONGATEDNESS",
"PR.AXIS_RECTANGULARITY",
"MAX.LENGTH_RECTANGULARITY",
"SCALED_VARIANCE_MAJOR",
"SCALED_VARIANCE_MINOR",
"SCALED_RADIUS_OF_GYRATION",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,185 |
75,169 | predictive_accuracy | accuracy_score | isolet | **Author**: Ron Cole and Mark Fanty (cole@cse.ogi.edu, fanty@cse.ogi.edu)
**Donor**: Tom Dietterich (tgd@cs.orst.edu)
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/ISOLET)
**Please cite**: UCI
### Description
ISOLET (Isolated Letter Speech Recognition) dataset was generated as follows: 150 subjec... | {0: [0 - f1 (numeric)],
1: [1 - f2 (numeric)],
2: [2 - f3 (numeric)],
3: [3 - f4 (numeric)],
4: [4 - f5 (numeric)],
5: [5 - f6 (numeric)],
6: [6 - f7 (numeric)],
7: [7 - f8 (numeric)],
8: [8 - f9 (numeric)],
9: [9 - f10 (numeric)],
10: [10 - f11 (numeric)],
11: [11 - f12 (numeric)],
12: [12 - f13 (numeric)]... | {'MajorityClassSize': 300.0,
'MaxNominalAttDistinctValues': 26.0,
'MinorityClassSize': 298.0,
'NumberOfClasses': 26.0,
'NumberOfFeatures': 618.0,
'NumberOfInstances': 7797.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 617.0,
'NumberOfSymbolicFeatures': 1... | isolet | [
"f1",
"f2",
"f3",
"f4",
"f5",
"f6",
"f7",
"f8",
"f9",
"f10",
"f11",
"f12",
"f13",
"f14",
"f15",
"f16",
"f17",
"f18",
"f19",
"f20",
"f21",
"f22",
"f23",
"f24",
"f25",
"f26",
"f27",
"f28",
"f29",
"f30",
"f31",
"f32",
"f33",
"f34",
"f35",
"f36",
"... | [
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f... | 3,186 |
362,675 | predictive_accuracy | accuracy_score | jannis_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset jannis (44131) 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': 55.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 54.0,
'NumberOfSymbolicFeatures': 1.0... | jannis_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",
"... | [
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f... | 3,187 |
362,740 | predictive_accuracy | accuracy_score | numerai28.6_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset numerai28.6 (23517) with
seed=4
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: ... | {0: [0 - attribute_0 (numeric)],
1: [1 - attribute_1 (numeric)],
2: [2 - attribute_2 (numeric)],
3: [3 - attribute_3 (numeric)],
4: [4 - attribute_4 (numeric)],
5: [5 - attribute_5 (numeric)],
6: [6 - attribute_6 (numeric)],
7: [7 - attribute_7 (numeric)],
8: [8 - attribute_8 (numeric)],
9: [9 - attribute_9 (n... | {'MajorityClassSize': 1010.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 990.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 22.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 21.0,
'NumberOfSymbolicFeatures': 1.0,... | numerai28.6_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"attribute_0",
"attribute_1",
"attribute_2",
"attribute_3",
"attribute_4",
"attribute_5",
"attribute_6",
"attribute_7",
"attribute_8",
"attribute_9",
"attribute_10",
"attribute_11",
"attribute_12",
"attribute_13",
"attribute_14",
"attribute_15",
"attribute_16",
"attribute_17",
"a... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,188 |
362,796 | predictive_accuracy | accuracy_score | vehicle_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset vehicle (54) 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 - COMPACTNESS (numeric)],
1: [1 - CIRCULARITY (numeric)],
2: [2 - DISTANCE_CIRCULARITY (numeric)],
3: [3 - RADIUS_RATIO (numeric)],
4: [4 - PR.AXIS_ASPECT_RATIO (numeric)],
5: [5 - MAX.LENGTH_ASPECT_RATIO (numeric)],
6: [6 - SCATTER_RATIO (numeric)],
7: [7 - ELONGATEDNESS (numeric)],
8: [8 - PR.AXIS_RECT... | {'MajorityClassSize': 218.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 199.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 19.0,
'NumberOfInstances': 846.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 18.0,
'NumberOfSymbolicFeatures': 1.0,
... | vehicle_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"COMPACTNESS",
"CIRCULARITY",
"DISTANCE_CIRCULARITY",
"RADIUS_RATIO",
"PR.AXIS_ASPECT_RATIO",
"MAX.LENGTH_ASPECT_RATIO",
"SCATTER_RATIO",
"ELONGATEDNESS",
"PR.AXIS_RECTANGULARITY",
"MAX.LENGTH_RECTANGULARITY",
"SCALED_VARIANCE_MAJOR",
"SCALED_VARIANCE_MINOR",
"SCALED_RADIUS_OF_GYRATION",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,189 |
362,738 | predictive_accuracy | accuracy_score | numerai28.6_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset numerai28.6 (23517) with
seed=1
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: ... | {0: [0 - attribute_0 (numeric)],
1: [1 - attribute_1 (numeric)],
2: [2 - attribute_2 (numeric)],
3: [3 - attribute_3 (numeric)],
4: [4 - attribute_4 (numeric)],
5: [5 - attribute_5 (numeric)],
6: [6 - attribute_6 (numeric)],
7: [7 - attribute_7 (numeric)],
8: [8 - attribute_8 (numeric)],
9: [9 - attribute_9 (n... | {'MajorityClassSize': 1010.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 990.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 22.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 21.0,
'NumberOfSymbolicFeatures': 1.0,... | numerai28.6_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"attribute_0",
"attribute_1",
"attribute_2",
"attribute_3",
"attribute_4",
"attribute_5",
"attribute_6",
"attribute_7",
"attribute_8",
"attribute_9",
"attribute_10",
"attribute_11",
"attribute_12",
"attribute_13",
"attribute_14",
"attribute_15",
"attribute_16",
"attribute_17",
"a... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,190 |
362,815 | predictive_accuracy | accuracy_score | arcene_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset arcene (41157) 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 - V387 (numeric)],
1: [1 - V396 (numeric)],
2: [2 - V547 (numeric)],
3: [3 - V569 (numeric)],
4: [4 - V765 (numeric)],
5: [5 - V911 (numeric)],
6: [6 - V967 (numeric)],
7: [7 - V1007 (numeric)],
8: [8 - V1042 (numeric)],
9: [9 - V1068 (numeric)],
10: [10 - V1083 (numeric)],
11: [11 - V1490 (numeric)],... | {'MajorityClassSize': 56.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 44.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0,
... | arcene_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V387",
"V396",
"V547",
"V569",
"V765",
"V911",
"V967",
"V1007",
"V1042",
"V1068",
"V1083",
"V1490",
"V1862",
"V1863",
"V1998",
"V2012",
"V2127",
"V2162",
"V2183",
"V2228",
"V2582",
"V2587",
"V2591",
"V2728",
"V2957",
"V3033",
"V3166",
"V3311",
"V3319",
"V33... | [
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 3,192 |
362,741 | predictive_accuracy | accuracy_score | numerai28.6_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset numerai28.6 (23517) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: ... | {0: [0 - attribute_0 (numeric)],
1: [1 - attribute_1 (numeric)],
2: [2 - attribute_2 (numeric)],
3: [3 - attribute_3 (numeric)],
4: [4 - attribute_4 (numeric)],
5: [5 - attribute_5 (numeric)],
6: [6 - attribute_6 (numeric)],
7: [7 - attribute_7 (numeric)],
8: [8 - attribute_8 (numeric)],
9: [9 - attribute_9 (n... | {'MajorityClassSize': 1010.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 990.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 22.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 21.0,
'NumberOfSymbolicFeatures': 1.0,... | numerai28.6_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"attribute_0",
"attribute_1",
"attribute_2",
"attribute_3",
"attribute_4",
"attribute_5",
"attribute_6",
"attribute_7",
"attribute_8",
"attribute_9",
"attribute_10",
"attribute_11",
"attribute_12",
"attribute_13",
"attribute_14",
"attribute_15",
"attribute_16",
"attribute_17",
"a... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,194 |
362,351 | predictive_accuracy | accuracy_score | mini_insect_1 | mini insect example dataset # 1 | {0: [0 - Data (string)],
1: [1 - Shape (string)],
2: [2 - Label (string)],
3: [3 - SuperCategory (string)]} | {'MajorityClassSize': 3.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 3.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 4.0,
'NumberOfInstances': 12.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 0.0,
'NumberOfSymbolicFeatures': 0.0,
'cost_m... | mini_insect_1 | [
"Data",
"Shape",
"SuperCategory"
] | [
false,
false,
false
] | 3,195 |
359,957 | predictive_accuracy | accuracy_score | cnae-9 | **Author**: Patrick Marques Ciarelli, Elias Oliviera
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/CNAE-9) - 2010
**Please cite**:
### Description
This is a data set containing 1080 documents of free text business descriptions of Brazilian companies categorized into a subset of 9 categories.
### ... | {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': 120.0,
'MaxNominalAttDistinctValues': 9.0,
'MinorityClassSize': 120.0,
'NumberOfClasses': 9.0,
'NumberOfFeatures': 857.0,
'NumberOfInstances': 1080.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 856.0,
'NumberOfSymbolicFeatures': 1.0... | cnae-9 | [
"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,
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false,
false,
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f... | 3,196 |
362,814 | predictive_accuracy | accuracy_score | arcene_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset arcene (41157) 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 - V198 (numeric)],
1: [1 - V274 (numeric)],
2: [2 - V346 (numeric)],
3: [3 - V395 (numeric)],
4: [4 - V542 (numeric)],
5: [5 - V617 (numeric)],
6: [6 - V623 (numeric)],
7: [7 - V851 (numeric)],
8: [8 - V917 (numeric)],
9: [9 - V1155 (numeric)],
10: [10 - V1164 (numeric)],
11: [11 - V1231 (numeric)],
... | {'MajorityClassSize': 56.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 44.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 100.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.0,
... | arcene_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V198",
"V274",
"V346",
"V395",
"V542",
"V617",
"V623",
"V851",
"V917",
"V1155",
"V1164",
"V1231",
"V1236",
"V1332",
"V1429",
"V1479",
"V1600",
"V2022",
"V2146",
"V2470",
"V2549",
"V2598",
"V2608",
"V2708",
"V2757",
"V2789",
"V2918",
"V2919",
"V3011",
"V3091... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,198 |
362,792 | predictive_accuracy | accuracy_score | vehicle_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset vehicle (54) 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 - COMPACTNESS (numeric)],
1: [1 - CIRCULARITY (numeric)],
2: [2 - DISTANCE_CIRCULARITY (numeric)],
3: [3 - RADIUS_RATIO (numeric)],
4: [4 - PR.AXIS_ASPECT_RATIO (numeric)],
5: [5 - MAX.LENGTH_ASPECT_RATIO (numeric)],
6: [6 - SCATTER_RATIO (numeric)],
7: [7 - ELONGATEDNESS (numeric)],
8: [8 - PR.AXIS_RECT... | {'MajorityClassSize': 218.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 199.0,
'NumberOfClasses': 4.0,
'NumberOfFeatures': 19.0,
'NumberOfInstances': 846.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 18.0,
'NumberOfSymbolicFeatures': 1.0,
... | vehicle_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"COMPACTNESS",
"CIRCULARITY",
"DISTANCE_CIRCULARITY",
"RADIUS_RATIO",
"PR.AXIS_ASPECT_RATIO",
"MAX.LENGTH_ASPECT_RATIO",
"SCATTER_RATIO",
"ELONGATEDNESS",
"PR.AXIS_RECTANGULARITY",
"MAX.LENGTH_RECTANGULARITY",
"SCALED_VARIANCE_MAJOR",
"SCALED_VARIANCE_MINOR",
"SCALED_RADIUS_OF_GYRATION",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,199 |
362,742 | predictive_accuracy | accuracy_score | numerai28.6_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset numerai28.6 (23517) with
seed=2
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max: ... | {0: [0 - attribute_0 (numeric)],
1: [1 - attribute_1 (numeric)],
2: [2 - attribute_2 (numeric)],
3: [3 - attribute_3 (numeric)],
4: [4 - attribute_4 (numeric)],
5: [5 - attribute_5 (numeric)],
6: [6 - attribute_6 (numeric)],
7: [7 - attribute_7 (numeric)],
8: [8 - attribute_8 (numeric)],
9: [9 - attribute_9 (n... | {'MajorityClassSize': 1010.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 990.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 22.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 21.0,
'NumberOfSymbolicFeatures': 1.0,... | numerai28.6_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"attribute_0",
"attribute_1",
"attribute_2",
"attribute_3",
"attribute_4",
"attribute_5",
"attribute_6",
"attribute_7",
"attribute_8",
"attribute_9",
"attribute_10",
"attribute_11",
"attribute_12",
"attribute_13",
"attribute_14",
"attribute_15",
"attribute_16",
"attribute_17",
"a... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,200 |
362,830 | predictive_accuracy | accuracy_score | eucalyptus_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset eucalyptus (188) 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 - Abbrev (nominal)],
1: [1 - Rep (numeric)],
2: [2 - Locality (nominal)],
3: [3 - Map_Ref (nominal)],
4: [4 - Latitude (nominal)],
5: [5 - Altitude (numeric)],
6: [6 - Rainfall (numeric)],
7: [7 - Frosts (numeric)],
8: [8 - Year (numeric)],
9: [9 - Sp (nominal)],
10: [10 - PMCno (numeric)],
11: [11 - ... | {'MajorityClassSize': 214.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 105.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 20.0,
'NumberOfInstances': 736.0,
'NumberOfInstancesWithMissingValues': 95.0,
'NumberOfMissingValues': 448.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures': 6.0... | eucalyptus_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Abbrev",
"Rep",
"Locality",
"Map_Ref",
"Latitude",
"Altitude",
"Rainfall",
"Frosts",
"Year",
"Sp",
"PMCno",
"DBH",
"Ht",
"Surv",
"Vig",
"Ins_res",
"Stem_Fm",
"Crown_Fm",
"Brnch_Fm"
] | [
true,
false,
true,
true,
true,
false,
false,
false,
false,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,201 |
362,773 | predictive_accuracy | accuracy_score | jasmine_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset jasmine (41143) 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 - V4 (nominal)],
1: [1 - V5 (nominal)],
2: [2 - V9 (nominal)],
3: [3 - V10 (nominal)],
4: [4 - V11 (nominal)],
5: [5 - V13 (numeric)],
6: [6 - V14 (nominal)],
7: [7 - V15 (nominal)],
8: [8 - V16 (nominal)],
9: [9 - V18 (nominal)],
10: [10 - V19 (nominal)],
11: [11 - V21 (nominal)],
12: [12 - V22 (nom... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 95.... | jasmine_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V4",
"V5",
"V9",
"V10",
"V11",
"V13",
"V14",
"V15",
"V16",
"V18",
"V19",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27",
"V29",
"V31",
"V33",
"V35",
"V36",
"V37",
"V39",
"V42",
"V44",
"V46",
"V47",
"V48",
"V49",
"V50",
"V52",
"V53",
"V54",
"V55... | [
true,
true,
true,
true,
true,
false,
true,
true,
true,
true,
true,
true,
true,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
true,
true,
f... | 3,202 |
362,810 | predictive_accuracy | accuracy_score | ada_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset ada (41156) 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 - 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': 1504.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 496.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 49.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 48.0,
'NumberOfSymbolicFeatures': 1.0,... | ada_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27",
"V28",
"V29",
"V30",
"V31",
"V32",
"V33",
"V34",
"V35",
"V36",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 3,203 |
362,769 | predictive_accuracy | accuracy_score | jasmine_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset jasmine (41143) 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 - V2 (nominal)],
1: [1 - V5 (nominal)],
2: [2 - V6 (nominal)],
3: [3 - V8 (nominal)],
4: [4 - V9 (nominal)],
5: [5 - V10 (nominal)],
6: [6 - V11 (nominal)],
7: [7 - V13 (numeric)],
8: [8 - V14 (nominal)],
9: [9 - V16 (nominal)],
10: [10 - V17 (nominal)],
11: [11 - V22 (nominal)],
12: [12 - V23 (numer... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 95.... | jasmine_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V2",
"V5",
"V6",
"V8",
"V9",
"V10",
"V11",
"V13",
"V14",
"V16",
"V17",
"V22",
"V23",
"V24",
"V25",
"V28",
"V29",
"V31",
"V32",
"V33",
"V34",
"V35",
"V36",
"V37",
"V40",
"V42",
"V44",
"V45",
"V46",
"V47",
"V48",
"V49",
"V50",
"V51",
"V54",
"V55",... | [
true,
true,
true,
true,
true,
true,
true,
false,
true,
true,
true,
true,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
true,
true,
true,
true,
true,
true,
true,
true,
false,
true,
true,
... | 3,204 |
362,829 | predictive_accuracy | accuracy_score | eucalyptus_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset eucalyptus (188) 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 - Abbrev (nominal)],
1: [1 - Rep (numeric)],
2: [2 - Locality (nominal)],
3: [3 - Map_Ref (nominal)],
4: [4 - Latitude (nominal)],
5: [5 - Altitude (numeric)],
6: [6 - Rainfall (numeric)],
7: [7 - Frosts (numeric)],
8: [8 - Year (numeric)],
9: [9 - Sp (nominal)],
10: [10 - PMCno (numeric)],
11: [11 - ... | {'MajorityClassSize': 214.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 105.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 20.0,
'NumberOfInstances': 736.0,
'NumberOfInstancesWithMissingValues': 95.0,
'NumberOfMissingValues': 448.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures': 6.0... | eucalyptus_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Abbrev",
"Rep",
"Locality",
"Map_Ref",
"Latitude",
"Altitude",
"Rainfall",
"Frosts",
"Year",
"Sp",
"PMCno",
"DBH",
"Ht",
"Surv",
"Vig",
"Ins_res",
"Stem_Fm",
"Crown_Fm",
"Brnch_Fm"
] | [
true,
false,
true,
true,
true,
false,
false,
false,
false,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,205 |
362,718 | predictive_accuracy | accuracy_score | KDDCup09_appetency_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset KDDCup09_appetency (1111) 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... | {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 - Var11 (numeric)],
7: [7 - Var12 (numeric)],
8: [8 - Var14 (numeric)],
9: [9 - Var16 (numeric)],
10: [10 - Var22 (numeric)],
11: [11 - Var25 (numeric)]... | {'MajorityClassSize': 1964.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 36.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 93.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 2000.0,
'NumberOfMissingValues': 127027.0,
'NumberOfNumericFeatures': 77.0,
'NumberOfSymbolicFeatures... | KDDCup09_appetency_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Var1",
"Var2",
"Var3",
"Var4",
"Var5",
"Var6",
"Var11",
"Var12",
"Var14",
"Var16",
"Var22",
"Var25",
"Var27",
"Var29",
"Var37",
"Var47",
"Var51",
"Var59",
"Var62",
"Var63",
"Var68",
"Var69",
"Var70",
"Var71",
"Var72",
"Var73",
"Var74",
"Var76",
"Var81",
"Va... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,206 |
362,831 | predictive_accuracy | accuracy_score | eucalyptus_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset eucalyptus (188) 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 - Abbrev (nominal)],
1: [1 - Rep (numeric)],
2: [2 - Locality (nominal)],
3: [3 - Map_Ref (nominal)],
4: [4 - Latitude (nominal)],
5: [5 - Altitude (numeric)],
6: [6 - Rainfall (numeric)],
7: [7 - Frosts (numeric)],
8: [8 - Year (numeric)],
9: [9 - Sp (nominal)],
10: [10 - PMCno (numeric)],
11: [11 - ... | {'MajorityClassSize': 214.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 105.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 20.0,
'NumberOfInstances': 736.0,
'NumberOfInstancesWithMissingValues': 95.0,
'NumberOfMissingValues': 448.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures': 6.0... | eucalyptus_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Abbrev",
"Rep",
"Locality",
"Map_Ref",
"Latitude",
"Altitude",
"Rainfall",
"Frosts",
"Year",
"Sp",
"PMCno",
"DBH",
"Ht",
"Surv",
"Vig",
"Ins_res",
"Stem_Fm",
"Crown_Fm",
"Brnch_Fm"
] | [
true,
false,
true,
true,
true,
false,
false,
false,
false,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,207 |
362,771 | predictive_accuracy | accuracy_score | jasmine_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset jasmine (41143) 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 (nominal)],
1: [1 - V3 (nominal)],
2: [2 - V4 (nominal)],
3: [3 - V5 (nominal)],
4: [4 - V6 (nominal)],
5: [5 - V7 (nominal)],
6: [6 - V8 (nominal)],
7: [7 - V9 (nominal)],
8: [8 - V10 (nominal)],
9: [9 - V12 (nominal)],
10: [10 - V13 (numeric)],
11: [11 - V15 (nominal)],
12: [12 - V16 (nominal)... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 95.... | jasmine_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V12",
"V13",
"V15",
"V16",
"V19",
"V23",
"V24",
"V25",
"V26",
"V27",
"V28",
"V29",
"V30",
"V31",
"V36",
"V37",
"V40",
"V41",
"V43",
"V45",
"V46",
"V47",
"V48",
"V49",
"V52",
"V53",
"V54",
... | [
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
true,
true,
true,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
false,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
... | 3,208 |
362,809 | predictive_accuracy | accuracy_score | ada_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset ada (41156) 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 - 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': 1504.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 496.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 49.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 48.0,
'NumberOfSymbolicFeatures': 1.0,... | ada_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",
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"... | [
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false,
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false,
false,
false,
f... | 3,210 |
362,840 | predictive_accuracy | accuracy_score | blood-transfusion-service-center_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset blood-transfusion-service-center (1464) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - Class (nominal)]} | {'MajorityClassSize': 570.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 178.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 748.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | blood-transfusion-service-center_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4"
] | [
false,
false,
false,
false
] | 3,211 |
362,785 | predictive_accuracy | accuracy_score | gina_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset gina (41158) 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 - V3 (numeric)],
1: [1 - V5 (numeric)],
2: [2 - V8 (numeric)],
3: [3 - V15 (numeric)],
4: [4 - V21 (numeric)],
5: [5 - V26 (numeric)],
6: [6 - V31 (numeric)],
7: [7 - V36 (numeric)],
8: [8 - V47 (numeric)],
9: [9 - V66 (numeric)],
10: [10 - V70 (numeric)],
11: [11 - V73 (numeric)],
12: [12 - V81 (num... | {'MajorityClassSize': 1017.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 983.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | gina_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V3",
"V5",
"V8",
"V15",
"V21",
"V26",
"V31",
"V36",
"V47",
"V66",
"V70",
"V73",
"V81",
"V82",
"V114",
"V127",
"V155",
"V159",
"V219",
"V236",
"V237",
"V243",
"V248",
"V255",
"V270",
"V273",
"V293",
"V308",
"V316",
"V324",
"V340",
"V352",
"V353",
"V3... | [
false,
false,
false,
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false,
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false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 3,212 |
362,811 | predictive_accuracy | accuracy_score | ada_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset ada (41156) 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 - 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': 1504.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 496.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 49.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 48.0,
'NumberOfSymbolicFeatures': 1.0,... | ada_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,
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false,
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false,
false,
false,
false,
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false,
false,
false,
false,
f... | 3,213 |
146,597 | 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... | 3,214 |
362,812 | predictive_accuracy | accuracy_score | ada_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset ada (41156) 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 - 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': 1504.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 496.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 49.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 48.0,
'NumberOfSymbolicFeatures': 1.0,... | ada_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,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 3,215 |
362,768 | predictive_accuracy | accuracy_score | jasmine_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset jasmine (41143) 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 (nominal)],
1: [1 - V2 (nominal)],
2: [2 - V3 (nominal)],
3: [3 - V4 (nominal)],
4: [4 - V7 (nominal)],
5: [5 - V9 (nominal)],
6: [6 - V10 (nominal)],
7: [7 - V11 (nominal)],
8: [8 - V12 (nominal)],
9: [9 - V13 (numeric)],
10: [10 - V14 (nominal)],
11: [11 - V15 (nominal)],
12: [12 - V16 (nomina... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 95.... | jasmine_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V7",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V19",
"V24",
"V25",
"V28",
"V29",
"V30",
"V32",
"V33",
"V34",
"V35",
"V36",
"V37",
"V39",
"V40",
"V42",
"V43",
"V44",
"V45",
"V48",
"V50",
"V52",
"V53",
"V54",
... | [
true,
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true,
true,
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true,
true,
true,
false,
true,
false,
true,
true,
true,
true,
true,
false,
true,
false,
... | 3,216 |
362,770 | predictive_accuracy | accuracy_score | jasmine_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset jasmine (41143) 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 - V4 (nominal)],
1: [1 - V5 (nominal)],
2: [2 - V6 (nominal)],
3: [3 - V8 (nominal)],
4: [4 - V10 (nominal)],
5: [5 - V11 (nominal)],
6: [6 - V13 (numeric)],
7: [7 - V15 (nominal)],
8: [8 - V16 (nominal)],
9: [9 - V17 (nominal)],
10: [10 - V18 (nominal)],
11: [11 - V20 (nominal)],
12: [12 - V21 (nomi... | {'MajorityClassSize': 1000.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 1000.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 6.0,
'NumberOfSymbolicFeatures': 95.... | jasmine_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V4",
"V5",
"V6",
"V8",
"V10",
"V11",
"V13",
"V15",
"V16",
"V17",
"V18",
"V20",
"V21",
"V22",
"V24",
"V27",
"V29",
"V30",
"V31",
"V32",
"V33",
"V35",
"V37",
"V38",
"V41",
"V43",
"V45",
"V47",
"V48",
"V49",
"V50",
"V51",
"V52",
"V53",
"V55",
"V56"... | [
true,
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true,
true,
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true,
true,
true,
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true,
true,
true,
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true,
true,
true,
true,
true,
true,
false,
false,
true,
true,
true,
true,
true,
true,
true,
true,
false,
true,
true,
false,
... | 3,217 |
362,842 | predictive_accuracy | accuracy_score | blood-transfusion-service-center_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset blood-transfusion-service-center (1464) with
seed=4
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - Class (nominal)]} | {'MajorityClassSize': 570.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 178.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 748.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | blood-transfusion-service-center_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4"
] | [
false,
false,
false,
false
] | 3,218 |
362,783 | predictive_accuracy | accuracy_score | gina_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset gina (41158) 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 - V37 (numeric)],
1: [1 - V38 (numeric)],
2: [2 - V49 (numeric)],
3: [3 - V55 (numeric)],
4: [4 - V74 (numeric)],
5: [5 - V81 (numeric)],
6: [6 - V93 (numeric)],
7: [7 - V96 (numeric)],
8: [8 - V98 (numeric)],
9: [9 - V100 (numeric)],
10: [10 - V135 (numeric)],
11: [11 - V167 (numeric)],
12: [12 - V1... | {'MajorityClassSize': 1017.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 983.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | gina_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V37",
"V38",
"V49",
"V55",
"V74",
"V81",
"V93",
"V96",
"V98",
"V100",
"V135",
"V167",
"V171",
"V180",
"V189",
"V198",
"V203",
"V207",
"V209",
"V229",
"V233",
"V245",
"V251",
"V261",
"V273",
"V293",
"V295",
"V304",
"V316",
"V327",
"V345",
"V356",
"V363... | [
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,
false,
f... | 3,219 |
362,784 | predictive_accuracy | accuracy_score | gina_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset gina (41158) 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 - V19 (numeric)],
1: [1 - V25 (numeric)],
2: [2 - V31 (numeric)],
3: [3 - V38 (numeric)],
4: [4 - V53 (numeric)],
5: [5 - V57 (numeric)],
6: [6 - V60 (numeric)],
7: [7 - V77 (numeric)],
8: [8 - V85 (numeric)],
9: [9 - V108 (numeric)],
10: [10 - V109 (numeric)],
11: [11 - V112 (numeric)],
12: [12 - V1... | {'MajorityClassSize': 1017.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 983.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | gina_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V19",
"V25",
"V31",
"V38",
"V53",
"V57",
"V60",
"V77",
"V85",
"V108",
"V109",
"V112",
"V113",
"V122",
"V127",
"V144",
"V150",
"V185",
"V206",
"V220",
"V228",
"V239",
"V242",
"V250",
"V257",
"V271",
"V273",
"V274",
"V275",
"V296",
"V300",
"V331",
"V341... | [
false,
false,
false,
false,
false,
false,
false,
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false,
false,
false,
false,
false,
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false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 3,220 |
362,788 | predictive_accuracy | accuracy_score | gina_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset gina (41158) 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 - V26 (numeric)],
1: [1 - V34 (numeric)],
2: [2 - V55 (numeric)],
3: [3 - V71 (numeric)],
4: [4 - V75 (numeric)],
5: [5 - V83 (numeric)],
6: [6 - V110 (numeric)],
7: [7 - V123 (numeric)],
8: [8 - V124 (numeric)],
9: [9 - V126 (numeric)],
10: [10 - V154 (numeric)],
11: [11 - V155 (numeric)],
12: [12 -... | {'MajorityClassSize': 1017.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 983.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | gina_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V26",
"V34",
"V55",
"V71",
"V75",
"V83",
"V110",
"V123",
"V124",
"V126",
"V154",
"V155",
"V162",
"V165",
"V172",
"V183",
"V187",
"V191",
"V196",
"V207",
"V209",
"V214",
"V250",
"V252",
"V277",
"V302",
"V325",
"V333",
"V334",
"V337",
"V348",
"V349",
"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... | 3,221 |
362,841 | predictive_accuracy | accuracy_score | blood-transfusion-service-center_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset blood-transfusion-service-center (1464) with
seed=2
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - Class (nominal)]} | {'MajorityClassSize': 570.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 178.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 748.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | blood-transfusion-service-center_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4"
] | [
false,
false,
false,
false
] | 3,222 |
362,808 | predictive_accuracy | accuracy_score | ada_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset ada (41156) 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 - 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': 1504.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 496.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 49.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 48.0,
'NumberOfSymbolicFeatures': 1.0,... | ada_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... | 3,223 |
362,839 | predictive_accuracy | accuracy_score | blood-transfusion-service-center_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset blood-transfusion-service-center (1464) with
seed=1
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - Class (nominal)]} | {'MajorityClassSize': 570.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 178.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 748.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | blood-transfusion-service-center_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4"
] | [
false,
false,
false,
false
] | 3,224 |
362,861 | predictive_accuracy | accuracy_score | cmc_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset cmc (23) 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 - Wifes_age (numeric)],
1: [1 - Wifes_education (nominal)],
2: [2 - Husbands_education (nominal)],
3: [3 - Number_of_children_ever_born (numeric)],
4: [4 - Wifes_religion (nominal)],
5: [5 - Wifes_now_working%3F (nominal)],
6: [6 - Husbands_occupation (nominal)],
7: [7 - Standard-of-living_index (nominal)... | {'MajorityClassSize': 629.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 333.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 1473.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 8.0,
... | cmc_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Wifes_age",
"Wifes_education",
"Husbands_education",
"Number_of_children_ever_born",
"Wifes_religion",
"Wifes_now_working%3F",
"Husbands_occupation",
"Standard-of-living_index",
"Media_exposure"
] | [
false,
true,
true,
false,
true,
true,
true,
true,
true
] | 3,225 |
362,780 | predictive_accuracy | accuracy_score | dionis_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset dionis (41167) 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': 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_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27",
"V28",
"V29",
"V30",
"V31",
"V32",
"V33",
"V34",
"V35",
"V36",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 3,226 |
362,858 | predictive_accuracy | accuracy_score | cmc_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset cmc (23) 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 - Wifes_age (numeric)],
1: [1 - Wifes_education (nominal)],
2: [2 - Husbands_education (nominal)],
3: [3 - Number_of_children_ever_born (numeric)],
4: [4 - Wifes_religion (nominal)],
5: [5 - Wifes_now_working%3F (nominal)],
6: [6 - Husbands_occupation (nominal)],
7: [7 - Standard-of-living_index (nominal)... | {'MajorityClassSize': 629.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 333.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 1473.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 8.0,
... | cmc_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Wifes_age",
"Wifes_education",
"Husbands_education",
"Number_of_children_ever_born",
"Wifes_religion",
"Wifes_now_working%3F",
"Husbands_occupation",
"Standard-of-living_index",
"Media_exposure"
] | [
false,
true,
true,
false,
true,
true,
true,
true,
true
] | 3,227 |
362,835 | predictive_accuracy | accuracy_score | eucalyptus_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset eucalyptus (188) 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 - Abbrev (nominal)],
1: [1 - Rep (numeric)],
2: [2 - Locality (nominal)],
3: [3 - Map_Ref (nominal)],
4: [4 - Latitude (nominal)],
5: [5 - Altitude (numeric)],
6: [6 - Rainfall (numeric)],
7: [7 - Frosts (numeric)],
8: [8 - Year (numeric)],
9: [9 - Sp (nominal)],
10: [10 - PMCno (numeric)],
11: [11 - ... | {'MajorityClassSize': 214.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 105.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 20.0,
'NumberOfInstances': 736.0,
'NumberOfInstancesWithMissingValues': 95.0,
'NumberOfMissingValues': 448.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures': 6.0... | eucalyptus_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Abbrev",
"Rep",
"Locality",
"Map_Ref",
"Latitude",
"Altitude",
"Rainfall",
"Frosts",
"Year",
"Sp",
"PMCno",
"DBH",
"Ht",
"Surv",
"Vig",
"Ins_res",
"Stem_Fm",
"Crown_Fm",
"Brnch_Fm"
] | [
true,
false,
true,
true,
true,
false,
false,
false,
false,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,228 |
362,862 | predictive_accuracy | accuracy_score | cmc_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset cmc (23) 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 - Wifes_age (numeric)],
1: [1 - Wifes_education (nominal)],
2: [2 - Husbands_education (nominal)],
3: [3 - Number_of_children_ever_born (numeric)],
4: [4 - Wifes_religion (nominal)],
5: [5 - Wifes_now_working%3F (nominal)],
6: [6 - Husbands_occupation (nominal)],
7: [7 - Standard-of-living_index (nominal)... | {'MajorityClassSize': 629.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 333.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 1473.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 8.0,
... | cmc_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Wifes_age",
"Wifes_education",
"Husbands_education",
"Number_of_children_ever_born",
"Wifes_religion",
"Wifes_now_working%3F",
"Husbands_occupation",
"Standard-of-living_index",
"Media_exposure"
] | [
false,
true,
true,
false,
true,
true,
true,
true,
true
] | 3,229 |
362,787 | predictive_accuracy | accuracy_score | ozone-level-8hr_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset ozone-level-8hr (1487) 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_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_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... | 3,230 |
362,822 | predictive_accuracy | accuracy_score | Diabetes130US_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Diabetes130US (4541) with
seed=4
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max:... | {0: [0 - encounter_id (numeric)],
1: [1 - patient_nbr (numeric)],
2: [2 - race (nominal)],
3: [3 - gender (nominal)],
4: [4 - age (nominal)],
5: [5 - weight (nominal)],
6: [6 - admission_type_id (numeric)],
7: [7 - discharge_disposition_id (numeric)],
8: [8 - admission_source_id (numeric)],
9: [9 - time_in_hos... | {'MajorityClassSize': 1078.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 223.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 50.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 1976.0,
'NumberOfMissingValues': 3764.0,
'NumberOfNumericFeatures': 13.0,
'NumberOfSymbolicFeatures'... | Diabetes130US_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"encounter_id",
"patient_nbr",
"race",
"gender",
"age",
"weight",
"admission_type_id",
"discharge_disposition_id",
"admission_source_id",
"time_in_hospital",
"payer_code",
"medical_specialty",
"num_lab_procedures",
"num_procedures",
"num_medications",
"number_outpatient",
"number_eme... | [
false,
false,
true,
true,
true,
true,
false,
false,
false,
false,
true,
true,
false,
false,
false,
false,
false,
false,
true,
true,
true,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
... | 3,232 |
362,837 | predictive_accuracy | accuracy_score | blood-transfusion-service-center_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset blood-transfusion-service-center (1464) with
seed=0
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
... | {0: [0 - V1 (numeric)],
1: [1 - V2 (numeric)],
2: [2 - V3 (numeric)],
3: [3 - V4 (numeric)],
4: [4 - Class (nominal)]} | {'MajorityClassSize': 570.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 178.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 5.0,
'NumberOfInstances': 748.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 4.0,
'NumberOfSymbolicFeatures': 1.0,
'c... | blood-transfusion-service-center_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4"
] | [
false,
false,
false,
false
] | 3,233 |
362,823 | predictive_accuracy | accuracy_score | micro-mass_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset micro-mass (1515) 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 - V7 (numeric)],
1: [1 - V11 (numeric)],
2: [2 - V27 (numeric)],
3: [3 - V35 (numeric)],
4: [4 - V41 (numeric)],
5: [5 - V62 (numeric)],
6: [6 - V68 (numeric)],
7: [7 - V76 (numeric)],
8: [8 - V92 (numeric)],
9: [9 - V98 (numeric)],
10: [10 - V102 (numeric)],
11: [11 - V108 (numeric)],
12: [12 - V152... | {'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_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V7",
"V11",
"V27",
"V35",
"V41",
"V62",
"V68",
"V76",
"V92",
"V98",
"V102",
"V108",
"V152",
"V168",
"V194",
"V213",
"V259",
"V289",
"V299",
"V307",
"V316",
"V321",
"V335",
"V364",
"V388",
"V405",
"V406",
"V416",
"V428",
"V433",
"V450",
"V468",
"V469",... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,234 |
362,820 | predictive_accuracy | accuracy_score | Diabetes130US_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Diabetes130US (4541) with
seed=2
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max:... | {0: [0 - encounter_id (numeric)],
1: [1 - patient_nbr (numeric)],
2: [2 - race (nominal)],
3: [3 - gender (nominal)],
4: [4 - age (nominal)],
5: [5 - weight (nominal)],
6: [6 - admission_type_id (numeric)],
7: [7 - discharge_disposition_id (numeric)],
8: [8 - admission_source_id (numeric)],
9: [9 - time_in_hos... | {'MajorityClassSize': 1078.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 223.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 50.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 1984.0,
'NumberOfMissingValues': 3820.0,
'NumberOfNumericFeatures': 13.0,
'NumberOfSymbolicFeatures'... | Diabetes130US_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"encounter_id",
"patient_nbr",
"race",
"gender",
"age",
"weight",
"admission_type_id",
"discharge_disposition_id",
"admission_source_id",
"time_in_hospital",
"payer_code",
"medical_specialty",
"num_lab_procedures",
"num_procedures",
"num_medications",
"number_outpatient",
"number_eme... | [
false,
false,
true,
true,
true,
true,
false,
false,
false,
false,
true,
true,
false,
false,
false,
false,
false,
false,
true,
true,
true,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
... | 3,235 |
362,782 | predictive_accuracy | accuracy_score | dionis_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset dionis (41167) 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': 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_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... | 3,236 |
362,833 | predictive_accuracy | accuracy_score | eucalyptus_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset eucalyptus (188) 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 - Abbrev (nominal)],
1: [1 - Rep (numeric)],
2: [2 - Locality (nominal)],
3: [3 - Map_Ref (nominal)],
4: [4 - Latitude (nominal)],
5: [5 - Altitude (numeric)],
6: [6 - Rainfall (numeric)],
7: [7 - Frosts (numeric)],
8: [8 - Year (numeric)],
9: [9 - Sp (nominal)],
10: [10 - PMCno (numeric)],
11: [11 - ... | {'MajorityClassSize': 214.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 105.0,
'NumberOfClasses': 5.0,
'NumberOfFeatures': 20.0,
'NumberOfInstances': 736.0,
'NumberOfInstancesWithMissingValues': 95.0,
'NumberOfMissingValues': 448.0,
'NumberOfNumericFeatures': 14.0,
'NumberOfSymbolicFeatures': 6.0... | eucalyptus_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Abbrev",
"Rep",
"Locality",
"Map_Ref",
"Latitude",
"Altitude",
"Rainfall",
"Frosts",
"Year",
"Sp",
"PMCno",
"DBH",
"Ht",
"Surv",
"Vig",
"Ins_res",
"Stem_Fm",
"Crown_Fm",
"Brnch_Fm"
] | [
true,
false,
true,
true,
true,
false,
false,
false,
false,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false
] | 3,238 |
362,819 | predictive_accuracy | accuracy_score | Diabetes130US_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Diabetes130US (4541) with
seed=1
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max:... | {0: [0 - encounter_id (numeric)],
1: [1 - patient_nbr (numeric)],
2: [2 - race (nominal)],
3: [3 - gender (nominal)],
4: [4 - age (nominal)],
5: [5 - weight (nominal)],
6: [6 - admission_type_id (numeric)],
7: [7 - discharge_disposition_id (numeric)],
8: [8 - admission_source_id (numeric)],
9: [9 - time_in_hos... | {'MajorityClassSize': 1078.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 223.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 50.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 1979.0,
'NumberOfMissingValues': 3790.0,
'NumberOfNumericFeatures': 13.0,
'NumberOfSymbolicFeatures'... | Diabetes130US_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"encounter_id",
"patient_nbr",
"race",
"gender",
"age",
"weight",
"admission_type_id",
"discharge_disposition_id",
"admission_source_id",
"time_in_hospital",
"payer_code",
"medical_specialty",
"num_lab_procedures",
"num_procedures",
"num_medications",
"number_outpatient",
"number_eme... | [
false,
false,
true,
true,
true,
true,
false,
false,
false,
false,
true,
true,
false,
false,
false,
false,
false,
false,
true,
true,
true,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
... | 3,239 |
362,764 | predictive_accuracy | accuracy_score | fabert_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset fabert (41164) 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 - V15 (numeric)],
1: [1 - V20 (numeric)],
2: [2 - V25 (numeric)],
3: [3 - V31 (numeric)],
4: [4 - V44 (numeric)],
5: [5 - V47 (numeric)],
6: [6 - V49 (numeric)],
7: [7 - V62 (numeric)],
8: [8 - V70 (numeric)],
9: [9 - V88 (numeric)],
10: [10 - V89 (numeric)],
11: [11 - V91 (numeric)],
12: [12 - V99 (... | {'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_1_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V15",
"V20",
"V25",
"V31",
"V44",
"V47",
"V49",
"V62",
"V70",
"V88",
"V89",
"V91",
"V99",
"V102",
"V119",
"V122",
"V151",
"V169",
"V177",
"V184",
"V195",
"V205",
"V209",
"V210",
"V221",
"V222",
"V224",
"V240",
"V245",
"V272",
"V280",
"V282",
"V287",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,240 |
362,786 | predictive_accuracy | accuracy_score | gina_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset gina (41158) 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 - V2 (numeric)],
1: [1 - V5 (numeric)],
2: [2 - V29 (numeric)],
3: [3 - V30 (numeric)],
4: [4 - V35 (numeric)],
5: [5 - V40 (numeric)],
6: [6 - V71 (numeric)],
7: [7 - V75 (numeric)],
8: [8 - V83 (numeric)],
9: [9 - V85 (numeric)],
10: [10 - V99 (numeric)],
11: [11 - V102 (numeric)],
12: [12 - V128 (... | {'MajorityClassSize': 1017.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 983.0,
'NumberOfClasses': 2.0,
'NumberOfFeatures': 101.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 100.0,
'NumberOfSymbolicFeatures': 1.... | gina_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V2",
"V5",
"V29",
"V30",
"V35",
"V40",
"V71",
"V75",
"V83",
"V85",
"V99",
"V102",
"V128",
"V142",
"V156",
"V157",
"V159",
"V177",
"V195",
"V207",
"V208",
"V216",
"V226",
"V234",
"V235",
"V237",
"V257",
"V267",
"V274",
"V278",
"V282",
"V289",
"V290",
... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,241 |
362,865 | predictive_accuracy | accuracy_score | car_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset car (40975) 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 - 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_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"buying",
"maint",
"doors",
"persons",
"lug_boot",
"safety"
] | [
true,
true,
true,
true,
true,
true
] | 3,242 |
362,779 | predictive_accuracy | accuracy_score | dionis_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset dionis (41167) 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': 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_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... | 3,243 |
362,860 | predictive_accuracy | accuracy_score | cmc_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset cmc (23) 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 - Wifes_age (numeric)],
1: [1 - Wifes_education (nominal)],
2: [2 - Husbands_education (nominal)],
3: [3 - Number_of_children_ever_born (numeric)],
4: [4 - Wifes_religion (nominal)],
5: [5 - Wifes_now_working%3F (nominal)],
6: [6 - Husbands_occupation (nominal)],
7: [7 - Standard-of-living_index (nominal)... | {'MajorityClassSize': 629.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 333.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 1473.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 8.0,
... | cmc_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Wifes_age",
"Wifes_education",
"Husbands_education",
"Number_of_children_ever_born",
"Wifes_religion",
"Wifes_now_working%3F",
"Husbands_occupation",
"Standard-of-living_index",
"Media_exposure"
] | [
false,
true,
true,
false,
true,
true,
true,
true,
true
] | 3,244 |
362,818 | predictive_accuracy | accuracy_score | Diabetes130US_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Diabetes130US (4541) with
seed=0
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max:... | {0: [0 - encounter_id (numeric)],
1: [1 - patient_nbr (numeric)],
2: [2 - race (nominal)],
3: [3 - gender (nominal)],
4: [4 - age (nominal)],
5: [5 - weight (nominal)],
6: [6 - admission_type_id (numeric)],
7: [7 - discharge_disposition_id (numeric)],
8: [8 - admission_source_id (numeric)],
9: [9 - time_in_hos... | {'MajorityClassSize': 1078.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 223.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 50.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 1991.0,
'NumberOfMissingValues': 3850.0,
'NumberOfNumericFeatures': 13.0,
'NumberOfSymbolicFeatures'... | Diabetes130US_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"encounter_id",
"patient_nbr",
"race",
"gender",
"age",
"weight",
"admission_type_id",
"discharge_disposition_id",
"admission_source_id",
"time_in_hospital",
"payer_code",
"medical_specialty",
"num_lab_procedures",
"num_procedures",
"num_medications",
"number_outpatient",
"number_eme... | [
false,
false,
true,
true,
true,
true,
false,
false,
false,
false,
true,
true,
false,
false,
false,
false,
false,
false,
true,
true,
true,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
... | 3,245 |
362,868 | predictive_accuracy | accuracy_score | car_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset car (40975) 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 - 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_4_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"buying",
"maint",
"doors",
"persons",
"lug_boot",
"safety"
] | [
true,
true,
true,
true,
true,
true
] | 3,246 |
362,821 | predictive_accuracy | accuracy_score | Diabetes130US_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset Diabetes130US (4541) with
seed=3
args.nrows=2000
args.ncols=100
args.nclasses=10
args.no_stratify=True
Generated with the following source code:
```python
def subsample(
self,
seed: int,
nrows_max: int = 2_000,
ncols_max: int = 100,
nclasses_max:... | {0: [0 - encounter_id (numeric)],
1: [1 - patient_nbr (numeric)],
2: [2 - race (nominal)],
3: [3 - gender (nominal)],
4: [4 - age (nominal)],
5: [5 - weight (nominal)],
6: [6 - admission_type_id (numeric)],
7: [7 - discharge_disposition_id (numeric)],
8: [8 - admission_source_id (numeric)],
9: [9 - time_in_hos... | {'MajorityClassSize': 1078.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 223.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 50.0,
'NumberOfInstances': 2000.0,
'NumberOfInstancesWithMissingValues': 1977.0,
'NumberOfMissingValues': 3814.0,
'NumberOfNumericFeatures': 13.0,
'NumberOfSymbolicFeatures'... | Diabetes130US_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"encounter_id",
"patient_nbr",
"race",
"gender",
"age",
"weight",
"admission_type_id",
"discharge_disposition_id",
"admission_source_id",
"time_in_hospital",
"payer_code",
"medical_specialty",
"num_lab_procedures",
"num_procedures",
"num_medications",
"number_outpatient",
"number_eme... | [
false,
false,
true,
true,
true,
true,
false,
false,
false,
false,
true,
true,
false,
false,
false,
false,
false,
false,
true,
true,
true,
false,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
true,
... | 3,247 |
362,790 | predictive_accuracy | accuracy_score | ozone-level-8hr_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset ozone-level-8hr (1487) 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_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_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27",
"V28",
"V29",
"V30",
"V31",
"V32",
"V33",
"V34",
"V35",
"V36",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 3,248 |
146,060 | precision | precision_score | isolet | **Author**: Ron Cole and Mark Fanty (cole@cse.ogi.edu, fanty@cse.ogi.edu)
**Donor**: Tom Dietterich (tgd@cs.orst.edu)
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/ISOLET)
**Please cite**: UCI
### Description
ISOLET (Isolated Letter Speech Recognition) dataset was generated as follows: 150 subjec... | {0: [0 - f1 (numeric)],
1: [1 - f2 (numeric)],
2: [2 - f3 (numeric)],
3: [3 - f4 (numeric)],
4: [4 - f5 (numeric)],
5: [5 - f6 (numeric)],
6: [6 - f7 (numeric)],
7: [7 - f8 (numeric)],
8: [8 - f9 (numeric)],
9: [9 - f10 (numeric)],
10: [10 - f11 (numeric)],
11: [11 - f12 (numeric)],
12: [12 - f13 (numeric)]... | {'MajorityClassSize': 300.0,
'MaxNominalAttDistinctValues': 26.0,
'MinorityClassSize': 298.0,
'NumberOfClasses': 26.0,
'NumberOfFeatures': 618.0,
'NumberOfInstances': 7797.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 617.0,
'NumberOfSymbolicFeatures': 1... | isolet | [
"f1",
"f2",
"f3",
"f4",
"f5",
"f6",
"f7",
"f8",
"f9",
"f10",
"f11",
"f12",
"f13",
"f14",
"f15",
"f16",
"f17",
"f18",
"f19",
"f20",
"f21",
"f22",
"f23",
"f24",
"f25",
"f26",
"f27",
"f28",
"f29",
"f30",
"f31",
"f32",
"f33",
"f34",
"f35",
"f36",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,249 |
362,859 | predictive_accuracy | accuracy_score | cmc_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset cmc (23) 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 - Wifes_age (numeric)],
1: [1 - Wifes_education (nominal)],
2: [2 - Husbands_education (nominal)],
3: [3 - Number_of_children_ever_born (numeric)],
4: [4 - Wifes_religion (nominal)],
5: [5 - Wifes_now_working%3F (nominal)],
6: [6 - Husbands_occupation (nominal)],
7: [7 - Standard-of-living_index (nominal)... | {'MajorityClassSize': 629.0,
'MaxNominalAttDistinctValues': nan,
'MinorityClassSize': 333.0,
'NumberOfClasses': 3.0,
'NumberOfFeatures': 10.0,
'NumberOfInstances': 1473.0,
'NumberOfInstancesWithMissingValues': 0.0,
'NumberOfMissingValues': 0.0,
'NumberOfNumericFeatures': 2.0,
'NumberOfSymbolicFeatures': 8.0,
... | cmc_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"Wifes_age",
"Wifes_education",
"Husbands_education",
"Number_of_children_ever_born",
"Wifes_religion",
"Wifes_now_working%3F",
"Husbands_occupation",
"Standard-of-living_index",
"Media_exposure"
] | [
false,
true,
true,
false,
true,
true,
true,
true,
true
] | 3,250 |
362,846 | predictive_accuracy | accuracy_score | qsar-biodeg_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset qsar-biodeg (1494) 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 - 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_3_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27",
"V28",
"V29",
"V30",
"V31",
"V32",
"V33",
"V34",
"V35",
"V36",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 3,251 |
362,845 | predictive_accuracy | accuracy_score | qsar-biodeg_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset qsar-biodeg (1494) 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 - 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_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V16",
"V17",
"V18",
"V19",
"V20",
"V21",
"V22",
"V23",
"V24",
"V25",
"V26",
"V27",
"V28",
"V29",
"V30",
"V31",
"V32",
"V33",
"V34",
"V35",
"V36",
"... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
f... | 3,252 |
362,847 | predictive_accuracy | accuracy_score | qsar-biodeg_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset qsar-biodeg (1494) 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 - 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_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... | 3,253 |
362,850 | predictive_accuracy | accuracy_score | cnae-9_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True | Subsampling of the dataset cnae-9 (1468) 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 - V32 (numeric)],
1: [1 - V34 (numeric)],
2: [2 - V43 (numeric)],
3: [3 - V49 (numeric)],
4: [4 - V65 (numeric)],
5: [5 - V71 (numeric)],
6: [6 - V82 (numeric)],
7: [7 - V83 (numeric)],
8: [8 - V86 (numeric)],
9: [9 - V87 (numeric)],
10: [10 - V88 (numeric)],
11: [11 - V118 (numeric)],
12: [12 - V145... | {'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_2_nrows_2000_nclasses_10_ncols_100_stratify_True | [
"V32",
"V34",
"V43",
"V49",
"V65",
"V71",
"V82",
"V83",
"V86",
"V87",
"V88",
"V118",
"V145",
"V150",
"V157",
"V166",
"V173",
"V178",
"V182",
"V185",
"V199",
"V203",
"V213",
"V221",
"V227",
"V238",
"V257",
"V266",
"V277",
"V288",
"V302",
"V311",
"V315",... | [
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
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... | 3,254 |
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