Datasets:
pfilonenko
/
ML_for_TwoSampleTesting

Dataset card Data Studio Files
xet
 Community
1
iter
int64
0
7.65k
sample
stringclasses
1 value
H0_H1
stringclasses
2 values
Hi
stringclasses
27 values
n1
int64
20
1k
n2
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1k
perc
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143
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Q_test
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MAX_Value_test
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MIN3_test
float64
0
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84.2
WLg_TaroneWare_test
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0
99.4
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WLg_Prentice_test
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0.452613
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0.20411
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7,646
train
H1
H23
300
300
50
47.666667
49.333333
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0.161614
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0.360058
0.474133
0.347338
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0.161614
0.840575
0.009552
0.013425
0.028744
0.153593
0.169833
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7,646
train
H0
H23
500
500
50
49.6
47.6
0.208821
-0.293514
0.091566
0.091383
0.920647
1.118034
0.8025
0.208821
0.293514
0.669483
0.008351
0.043154
0.08179
0.337565
0.318667
0.315944
7,646
train
H1
H23
500
500
50
47.6
46.4
-0.882471
0.923461
0.498375
0.498023
2.193337
4.772982
1.751591
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0.923461
0.189196
0.248027
0.776752
0.863519
0.07758
0.059384
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7,646
train
H0
H23
1,000
1,000
50
48.2
49.7
0.982288
-0.862084
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1.509405
1.512355
1.512397
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1.17426
0.339391
1.377201
1.00363
0.73311
1.332272
1.346585
0.955369
7,646
train
H1
H23
1,000
1,000
50
47.7
49.2
-0.122909
0.379167
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3.281154
3.171618
3.335644
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0.492344
0.188658
0.241931
0.003943
0.147579
1.925693
1.839027
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7,646
train
H0
H24
20
20
0
0
0
-1.054953
1.054953
1.860863
2.00673
8.924414
8.931587
9.003134
2.00673
1.860863
0.011092
4.026967
2.262112
1.121681
8.637742
8.517224
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7,646
train
H1
H24
20
20
0
0
0
-1.541855
1.541855
1.691657
1.733914
3.015917
3.125788
3.009112
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1.691657
0.118406
3.006459
2.706737
2.390728
2.553495
2.586823
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7,646
train
H0
H24
30
30
0
0
0
-0.118275
0.118275
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0.964923
0.955525
1.02948
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0.380255
0.597656
0.139951
0.007277
0.013898
0.532636
0.492177
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7,646
train
H1
H24
30
30
0
0
0
-1.448874
1.448874
1.366706
1.362929
2.042466
2.211982
2.134919
1.362929
1.448874
0.143988
1.857576
2.019957
2.105217
0.844613
0.860221
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7,646
train
H0
H24
50
50
0
0
0
-0.930666
0.930666
-0.181483
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5.708782
5.683339
5.531971
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0.930666
0.062914
0.033256
0.27164
0.86826
2.014334
1.84713
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7,646
train
H1
H24
50
50
0
0
0
1.427021
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2.101074
2.432208
2.075061
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1.427021
0.151512
1.773373
2.132897
2.047399
0.792107
0.796217
1.43421
7,646
train
H0
H24
75
75
0
0
0
-0.377752
0.377752
0.12277
0.121652
0.338764
0.332496
0.321438
0.121652
0.377752
0.706558
0.014799
0.086612
0.142367
0.018707
0.009483
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7,646
train
H1
H24
75
75
0
0
0
1.817345
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5.732057
8.581664
4.35776
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1.817345
0.035402
1.239323
2.962887
3.331038
0.044173
0.102225
1.811236
7,646
train
H0
H24
100
100
0
0
0
0.798988
-0.798988
-0.789761
-0.788925
0.811378
3.29908
0.672335
0.798988
0.798988
0.347771
0.622402
0.749555
0.637479
0.303187
0.404784
0.800993
7,646
train
H1
H24
100
100
0
0
0
0.561979
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-0.871424
0.898044
13.411585
0.934585
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0.861819
0.003826
0.759379
0.891742
0.317347
1.116917
1.396241
0.56339
7,646
train
H0
H24
150
150
0
0
0
-0.342097
0.342097
-0.556709
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4.634813
9.466864
3.420219
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0.556709
0.023686
0.315153
0.005852
0.116905
1.894797
1.732518
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7,646
train
H1
H24
150
150
0
0
0
1.388355
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2.819799
7.137358
1.935164
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1.388355
0.067646
1.543617
2.344387
1.93159
0.635034
0.784932
1.390674
7,646
train
H0
H24
200
200
0
0
0
-0.582973
0.582973
1.136846
1.135316
1.696239
1.667068
1.960719
-0.582973
1.136846
0.375176
1.288943
0.765785
0.339869
1.997288
1.997008
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7,646
train
H1
H24
200
200
0
0
0
-1.001606
1.001606
0.759297
0.761577
0.600678
10.612874
1.058615
-1.001606
1.001606
0.014014
0.58
0.616147
1.003482
0.096407
0.061698
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7,646
train
H0
H24
300
300
0
0
0
0.60007
-0.60007
-0.853247
-0.851875
0.757307
1.397698
0.805534
0.60007
0.853247
0.548126
0.725691
0.537754
0.359909
0.744616
0.804939
0.60057
7,646
train
H1
H24
300
300
0
0
0
-1.230755
1.230755
0.924932
0.926224
0.896595
7.359584
1.605322
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1.230755
0.061278
0.85789
0.894175
1.516444
0.12659
0.095551
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7,646
train
H0
H24
500
500
0
0
0
-1.121613
1.121613
1.468519
1.470376
2.34849
3.852468
2.26248
1.470376
1.468519
0.261788
2.162007
1.630733
1.258127
2.089753
2.012908
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7,646
train
H1
H24
500
500
0
0
0
0.057154
-0.057154
0.191408
0.191507
0.648898
1.132104
0.240748
0.057154
0.191408
0.76933
0.036675
0.008568
0.003267
0.146334
0.155407
0.057182
7,646
train
H0
H24
1,000
1,000
0
0
0
0.48013
-0.48013
-0.635318
-0.634783
0.413551
0.585063
0.422712
-0.634783
0.635318
0.63105
0.40295
0.396296
0.230491
0.375242
0.366558
0.48025
7,646
train
H1
H24
1,000
1,000
0
0
0
0.977297
-0.977297
-0.3804
-0.382549
6.10429
28.378949
1.870911
-0.382549
0.977297
0.000003
0.146344
1.268275
0.955863
0.067501
0.050529
0.977541
7,646
train
H0
H24
20
20
10
15
15
0.352965
-0.304276
-0.857616
-0.861099
2.203526
2.163148
2.269685
-0.861099
0.857616
0.321473
0.741492
0.305363
0.09249
1.721737
1.606842
0.321254
7,646
train
H1
H24
20
20
10
5
10
-1.196344
1.228227
0.353472
0.34751
3.811411
3.974201
4.194616
-1.196344
1.228227
0.122787
0.120763
0.761685
1.527381
0.640403
0.578773
-1.238437
7,646
train
H0
H24
30
30
10
13.333333
6.666667
-1.080683
1.142135
0.160638
0.159419
4.500507
4.363723
4.684593
-1.080683
1.142135
0.096107
0.025414
0.574672
1.305711
1.083099
0.964584
-1.155551
7,646
train
H1
H24
30
30
10
3.333333
3.333333
1.060437
-1.077019
-0.127402
-0.126322
4.079073
3.897549
4.201351
1.060437
1.077019
0.122374
0.015957
0.506803
1.160518
0.826868
0.701625
1.095574
7,646
train
H0
H24
50
50
10
14
4
-0.906094
0.89817
1.096921
1.094014
1.440354
6.646256
1.228201
-0.906094
1.096921
0.08407
1.196867
0.802503
0.804509
1.146625
0.961135
-0.913176
7,646
train
H1
H24
50
50
10
14
8
0.296814
-0.27668
-0.308839
-0.306324
0.133253
0.627747
0.096171
0.296814
0.308839
0.779928
0.093834
0.12476
0.07637
0.034525
0.042355
0.279412
7,646
train
H0
H24
75
75
10
17.333333
5.333333
-0.488579
0.439058
1.330195
1.328695
5.380572
8.638988
4.636967
1.328695
1.330195
0.034497
1.765432
0.558885
0.192342
4.184454
3.919387
-0.45944
7,646
train
H1
H24
75
75
10
16
13.333333
1.049658
-1.09144
-0.480522
-0.478141
2.253079
2.461564
2.266929
1.049658
1.09144
0.270041
0.228619
0.81233
1.191539
0.087917
0.074057
1.102968
7,646
train
H0
H24
100
100
10
13
14
-1.081349
1.11115
0.550038
0.547585
2.039017
1.939177
2.193191
-1.081349
1.11115
0.263227
0.29985
0.797648
1.234805
0.056328
0.042448
-1.118797
7,646
train
H1
H24
100
100
10
11
6
-0.98227
0.993742
0.836724
0.834355
0.98033
1.228708
0.975513
-0.98227
0.993742
0.317669
0.696149
0.943304
0.987196
0.220664
0.201478
-0.999259
7,646
train
H0
H24
150
150
10
10
8.666667
-0.254367
0.242793
0.503102
0.501507
0.50305
0.709058
0.447922
-0.254367
0.503102
0.799346
0.251509
0.115411
0.058895
0.473314
0.429568
-0.244834
7,646
train
H1
H24
150
150
10
12
12.666667
-0.844885
0.85146
0.805129
0.80566
0.651611
2.994422
0.723921
-0.844885
0.85146
0.389777
0.649087
0.611421
0.724432
0.334934
0.295368
-0.860021
7,646
train
H0
H24
200
200
10
12
10.5
-0.315085
0.30446
0.414108
0.412585
0.212736
0.816861
0.184945
0.412585
0.414108
0.758315
0.170226
0.107419
0.092621
0.195432
0.156925
-0.307694
7,646
train
H1
H24
200
200
10
10.5
12
1.839751
-1.837491
-1.713473
-1.716575
3.732042
4.955933
3.409868
1.839751
1.837491
0.064456
2.94663
3.649097
3.381159
1.294448
1.465269
1.849014
7,646
train
H0
H24
300
300
10
9.666667
10
-1.018679
1.023513
0.730339
0.729921
1.402549
2.059219
1.202406
0.729921
1.023513
0.303477
0.532785
0.953609
1.047742
0.044802
0.062233
-1.029005
7,646
train
H1
H24
300
300
10
13.333333
7.666667
0.123803
-0.135272
0.28473
0.284754
1.925622
9.427428
0.790755
0.123803
0.28473
0.024116
0.081085
0.026634
0.018306
0.421876
0.357369
0.133858
7,646
train
H0
H24
500
500
10
11.4
11
-1.555905
1.585936
0.844683
0.844534
3.243157
4.507976
3.973102
-1.555905
1.585936
0.110121
0.713237
1.609313
2.516262
0.043667
0.044543
-1.597649
7,646
train
H1
H24
500
500
10
8
10
1.185922
-1.161455
-1.310788
-1.313515
2.295634
9.666299
1.726968
-1.313515
1.310788
0.021626
1.725323
2.283515
1.350568
1.345461
1.418514
1.170624
7,646
train
H0
H24
1,000
1,000
10
9.4
9.2
-0.635983
0.633273
0.704204
0.703667
0.536038
1.239157
0.495475
0.703667
0.704204
0.524273
0.495147
0.548864
0.401024
0.362115
0.388768
-0.636772
7,646
train
H1
H24
1,000
1,000
10
10.3
9.6
0.96404
-0.907442
-1.674786
-1.679996
2.936923
15.474319
4.23479
-1.679996
1.674786
0.001453
2.822386
2.335898
0.824419
4.374779
4.518235
0.910668