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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0
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logrank_test
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0
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BN_MCE_test
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143
BN_SCE_test
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Q_test
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MAX_Value_test
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0
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MIN3_test
float64
0
1
WLg_logrank_test
float64
0
84.2
WLg_TaroneWare_test
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0
99.4
WLg_Breslow_test
float64
0
106
WLg_PetoPrentice_test
float64
0
71.9
WLg_Prentice_test
float64
0
71.6
WKM_test
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5.786672
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7,642
train
H1
H26
300
300
50
49
48.666667
0.758844
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6.342325
1.423359
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1.401361
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1.287756
7,642
train
H0
H26
500
500
50
50.4
51
-0.130244
0.283743
0.031314
0.031265
0.05332
0.561457
0.099224
0.031265
0.283743
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0.000977
0.017751
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7,642
train
H1
H26
500
500
50
49.8
48.2
1.520344
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8.420597
17.878138
10.285463
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2.291149
0.000466
0.305426
2.170895
5.258475
1.62702
1.652262
2.46527
7,642
train
H0
H26
1,000
1,000
50
49.4
51.8
0.71537
-0.559003
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1.986308
2.40484
1.834065
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1.039057
0.399703
1.07799
0.478231
0.31238
1.77534
1.745427
0.601068
7,642
train
H1
H26
1,000
1,000
50
51.9
54.4
1.166256
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4.899191
12.240591
6.050346
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1.853037
0.006603
0.213801
1.393063
3.433414
0.984948
1.004078
2.012021
7,642
train
H0
H27
20
20
0
0
0
-0.703302
0.703302
1.035445
1.027488
1.167619
1.371565
1.262751
1.027488
1.035445
0.476303
1.055732
0.800368
0.493251
1.457641
1.576123
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7,642
train
H1
H27
20
20
0
0
0
2.164007
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4.562542
6.170204
4.827294
2.164007
2.164007
0.02841
3.587681
4.381299
4.804805
1.179275
1.165543
2.191575
7,642
train
H0
H27
30
30
0
0
0
-0.206982
0.206982
0.084553
0.083001
0.098152
0.374214
0.085115
0.083001
0.206982
0.83466
0.006889
0.025108
0.042597
0.000039
0.005158
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7,642
train
H1
H27
30
30
0
0
0
0.887066
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0.261957
0.26315
6.229202
5.881756
6.323822
0.887066
0.887066
0.042345
0.069248
0.174182
0.78822
2.137046
1.947047
0.894551
7,642
train
H0
H27
50
50
0
0
0
0.110301
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0.425516
0.422017
1.114084
1.062165
1.176232
0.110301
0.425516
0.555373
0.178099
0.011467
0.012135
0.834195
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0.110857
7,642
train
H1
H27
50
50
0
0
0
0.068938
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0.892962
0.908804
5.539704
8.153041
4.800452
0.068938
0.892962
0.042952
0.825924
0.091374
0.004741
3.218763
2.990137
0.069285
7,642
train
H0
H27
75
75
0
0
0
-1.824862
1.824862
1.610024
1.612479
3.011386
3.595764
3.351144
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1.824862
0.068953
2.600089
2.991567
3.350715
0.859752
0.840599
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7,642
train
H1
H27
75
75
0
0
0
-0.986666
0.986666
0.362184
0.364137
3.298923
7.854928
2.111062
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0.986666
0.049107
0.132596
0.783893
0.975484
0.209604
0.124672
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7,642
train
H0
H27
100
100
0
0
0
0.566866
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0.331654
0.504868
0.335163
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0.566866
0.569839
0.188761
0.265726
0.320829
0.022763
0.038463
0.568289
7,642
train
H1
H27
100
100
0
0
0
1.729919
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2.224365
6.428679
3.106967
1.729919
1.729919
0.082872
1.855916
2.183191
3.003208
0.404174
0.339865
1.73426
7,642
train
H0
H27
150
150
0
0
0
-0.857239
0.857239
1.297082
1.299848
1.774261
4.19102
2.014389
1.299848
1.297082
0.241562
1.689604
1.254051
0.734618
2.025598
2.258726
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7,642
train
H1
H27
150
150
0
0
0
0.367388
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0.048966
2.120149
0.175523
0.367388
0.367388
0.547847
0.048475
0.046157
0.134931
0.000005
0.00346
0.368002
7,642
train
H0
H27
200
200
0
0
0
0.953169
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1.619414
4.696272
1.707072
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1.267656
0.195437
1.611016
1.456267
0.90859
1.568028
1.747001
0.954363
7,642
train
H1
H27
200
200
0
0
0
1.01804
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0.312938
0.314505
7.351801
7.151784
7.275115
1.01804
1.01804
0.026317
0.098913
0.260042
1.037107
2.523602
2.382676
1.019315
7,642
train
H0
H27
300
300
0
0
0
0.142717
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0.250832
0.250642
1.088959
2.291769
0.597056
0.142717
0.250832
0.5141
0.062821
0.006677
0.020354
0.360997
0.318124
0.142836
7,642
train
H1
H27
300
300
0
0
0
1.199668
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-0.514007
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0.919025
8.764617
2.590715
1.199668
1.199668
0.03259
0.263969
0.579302
1.440951
0.086545
0.120579
1.200669
7,642
train
H0
H27
500
500
0
0
0
-0.566281
0.566281
1.042394
1.042265
1.375479
1.287671
1.552042
1.042265
1.042394
0.460234
1.086315
0.649206
0.320608
1.563476
1.524495
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7,642
train
H1
H27
500
500
0
0
0
2.934764
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-2.021444
-2.025696
6.390471
14.227378
9.784007
2.934764
2.934764
0.002611
4.103445
5.830652
8.637831
0.385598
0.348204
2.936233
7,642
train
H0
H27
1,000
1,000
0
0
0
1.372552
-1.372552
-0.482557
-0.482194
3.300805
2.722966
3.898161
1.372552
1.372552
0.142405
0.232511
1.048637
1.884343
0.279497
0.258297
1.372895
7,642
train
H1
H27
1,000
1,000
0
0
0
3.883244
-3.883244
-2.522656
-2.530522
10.648163
28.755827
18.082845
3.883244
3.883244
0.000003
6.403541
9.432645
15.129874
0.342434
0.311279
3.884215
7,642
train
H0
H27
20
20
10
10
10
1.877416
-1.904644
-1.214809
-1.211782
5.102465
5.41065
4.74792
-1.211782
1.904644
0.054317
1.468415
3.005043
3.672994
0.045163
0.139694
1.924299
7,642
train
H1
H27
20
20
10
10
20
2.307701
-2.289605
-2.237553
-2.37951
5.681046
12.168696
5.763491
2.307701
2.289605
0.006827
5.662068
5.431882
5.404845
3.315027
3.150333
2.369542
7,642
train
H0
H27
30
30
10
23.333333
13.333333
-0.803246
0.755546
1.177134
1.178924
1.950005
2.958247
2.071137
1.178924
1.177134
0.355025
1.389861
0.958738
0.568405
2.357115
2.414944
-0.780623
7,642
train
H1
H27
30
30
10
13.333333
16.666667
0.601523
-0.605661
-0.364298
-0.360099
0.383343
2.90823
0.56991
0.601523
0.605661
0.405992
0.129671
0.208714
0.366063
0.006251
0.020066
0.617717
7,642
train
H0
H27
50
50
10
6
14
0.42434
-0.50339
0.566367
0.581872
7.079582
8.087592
6.577145
0.581872
0.566367
0.037307
0.338575
0.007671
0.253119
3.051012
2.835375
0.508722
7,642
train
H1
H27
50
50
10
22
10
0.059403
-0.107482
0.198392
0.198135
0.359228
0.918759
0.411877
0.198135
0.198392
0.813883
0.039258
0.000757
0.01151
0.28685
0.318547
0.106488
7,642
train
H0
H27
75
75
10
6.666667
12
0.845729
-0.822141
-0.954236
-0.952633
0.90751
1.582575
0.908149
-0.952633
0.954236
0.409397
0.907509
0.783542
0.675256
0.669928
0.802437
0.824954
7,642
train
H1
H27
75
75
10
10.666667
14.666667
0.377001
-0.356686
-0.826086
-0.82159
1.621367
2.303626
1.396988
-0.82159
0.826086
0.497334
0.67501
0.250102
0.126997
1.21858
1.078877
0.36244
7,642
train
H0
H27
100
100
10
15
12
-1.428479
1.380082
1.755418
1.770443
3.304869
3.751182
3.347085
1.770443
1.755418
0.163151
3.134469
2.485504
1.90686
3.157285
3.329831
-1.394553
7,642
train
H1
H27
100
100
10
16
7
0.174288
-0.175689
-0.242322
-0.243986
0.365553
8.411614
0.070836
-0.243986
0.242322
0.038228
0.059529
0.002273
0.03085
0.074785
0.034123
0.179004
7,642
train
H0
H27
150
150
10
12.666667
10
-1.649417
1.697724
0.825462
0.826695
5.477707
5.774589
5.050713
-1.649417
1.697724
0.08003
0.683424
2.072604
2.886628
0.188861
0.154477
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7,642
train
H1
H27
150
150
10
10
8
0.540318
-0.581956
0.250109
0.249263
1.755216
5.791435
2.897836
0.540318
0.581956
0.12221
0.062132
0.021341
0.339135
1.124853
1.287689
0.585535
7,642
train
H0
H27
200
200
10
10
11
0.85939
-0.882354
-0.479293
-0.478543
1.054297
1.62292
1.159652
0.85939
0.882354
0.374245
0.229004
0.550089
0.778433
0.008339
0.011412
0.88855
7,642
train
H1
H27
200
200
10
12
11.5
2.131628
-2.156261
-1.470062
-1.470963
4.670463
5.818845
5.570915
-1.470963
2.156261
0.02937
2.163731
3.41911
4.665893
0.126338
0.126465
2.17848
7,642
train
H0
H27
300
300
10
10.666667
7.333333
-1.030456
1.061535
0.450876
0.450759
1.722015
2.412973
2.128641
-1.030456
1.061535
0.285147
0.203183
0.66177
1.127097
0.108114
0.11431
-1.068827
7,642
train
H1
H27
300
300
10
11.666667
11.333333
1.180305
-1.262668
-0.044246
-0.044275
3.75523
8.783202
6.603106
1.180305
1.262668
0.032317
0.00196
0.35095
1.5985
1.43579
1.439414
1.271417
7,642
train
H0
H27
500
500
10
12
11.4
0.697228
-0.71757
-0.449365
-0.448893
0.574952
0.893212
0.646844
0.697228
0.71757
0.469873
0.201505
0.388318
0.514818
0.001337
0.001327
0.722686
7,642
train
H1
H27
500
500
10
11.6
11.2
3.220299
-3.323513
-1.43433
-1.433964
16.053094
23.395708
20.979984
3.220299
3.323513
0.000028
2.056254
6.24486
11.087579
0.717653
0.736205
3.349765
7,642
train
H0
H27
1,000
1,000
10
10.3
11.2
0.819704
-0.814743
-0.92188
-0.921312
0.8489
0.973914
0.848886
-0.921312
0.92188
0.411982
0.848816
0.807585
0.663847
0.6231
0.612918
0.820411
7,642
train
H1
H27
1,000
1,000
10
8.2
10.7
3.506152
-3.580831
-2.042207
-2.044409
10.64415
26.249129
17.926005
3.506152
3.580831
0.000008
4.179606
7.249147
12.859875
0.000282
0.000638
3.607664