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
int64
20
1k
perc
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0
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logrank_test
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0
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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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MIN3_test
float64
0
1
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0
84.2
WLg_TaroneWare_test
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0
99.4
WLg_Breslow_test
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0
106
WLg_PetoPrentice_test
float64
0
71.9
WLg_Prentice_test
float64
0
71.6
WKM_test
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7,647
train
H1
H15
300
300
50
49
52.333333
1.371548
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4.188231
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7,647
train
H0
H15
500
500
50
49.6
52
1.081705
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4.802433
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7,647
train
H1
H15
500
500
50
49
49.8
1.625564
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7.819454
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7,647
train
H0
H15
1,000
1,000
50
48.7
49.7
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4.456661
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1.817051
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7,647
train
H1
H15
1,000
1,000
50
50.1
49.3
2.426802
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24.477163
49.731247
27.055892
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3.134052
0
1.416189
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7,647
train
H0
H16
20
20
0
0
0
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7,647
train
H1
H16
20
20
0
0
0
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7,647
train
H0
H16
30
30
0
0
0
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0.937457
1.793418
2.717134
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0.878825
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1.806505
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7,647
train
H1
H16
30
30
0
0
0
0.221766
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7,647
train
H0
H16
50
50
0
0
0
1.888907
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5.171299
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4.388961
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7,647
train
H1
H16
50
50
0
0
0
0.682488
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3.270651
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1.279721
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7,647
train
H0
H16
75
75
0
0
0
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0.144711
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6.251824
6.424949
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0.989475
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1.007501
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4.103354
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7,647
train
H1
H16
75
75
0
0
0
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0.555901
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0.607319
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0.555901
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0.305622
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7,647
train
H0
H16
100
100
0
0
0
0.232122
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0.998375
1.016073
8.310006
9.759493
7.173204
0.232122
0.998375
0.020725
1.032403
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4.43601
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7,647
train
H1
H16
100
100
0
0
0
-1.282779
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0.530689
0.53165
4.077076
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1.282779
0.136162
0.282651
1.235672
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7,647
train
H0
H16
150
150
0
0
0
-1.408321
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1.923148
1.937254
4.033213
3.97094
4.107339
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1.923148
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3.752951
2.825833
1.985403
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7,647
train
H1
H16
150
150
0
0
0
1.761067
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3.989775
11.108737
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1.761067
0.011152
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7,647
train
H0
H16
200
200
0
0
0
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0.13924
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1.687549
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7,647
train
H1
H16
200
200
0
0
0
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1.071667
0.200774
0.200426
3.361131
3.633495
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1.071667
0.187664
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7,647
train
H0
H16
300
300
0
0
0
0.947205
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1.861407
3.068353
1.763592
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1.281183
0.343132
1.644926
1.171771
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1.589362
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7,647
train
H1
H16
300
300
0
0
0
-0.99572
0.99572
0.139568
0.139487
2.188618
5.771768
3.156126
0.139487
0.99572
0.123258
0.019457
0.391505
0.991593
0.592157
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7,647
train
H0
H16
500
500
0
0
0
1.744172
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3.142042
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3.20053
1.744172
1.744172
0.080977
2.926646
3.201388
3.044369
1.498596
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1.745045
7,647
train
H1
H16
500
500
0
0
0
-0.823801
0.823801
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5.522709
5.56792
7.313768
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0.823801
0.025813
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0.046613
0.679068
3.356108
3.317721
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7,647
train
H0
H16
1,000
1,000
0
0
0
-1.395629
1.395629
2.285335
2.291546
6.10884
9.314332
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2.291546
2.285335
0.025391
5.251182
3.317651
1.948165
6.691036
6.86924
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7,647
train
H1
H16
1,000
1,000
0
0
0
0.312007
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3.687987
9.493216
5.030368
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1.368695
0.023404
1.8804
0.65695
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4.306439
4.446045
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7,647
train
H0
H16
20
20
10
10
5
-0.618682
0.624677
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4.570243
6.695897
5.331141
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0.624677
0.06956
0.092816
0.034561
0.393674
2.102356
2.168638
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7,647
train
H1
H16
20
20
10
15
15
-1.18733
1.199806
0.979669
0.97092
1.556841
1.61858
1.509122
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1.199806
0.222308
0.942686
1.316073
1.440617
0.179979
0.252912
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7,647
train
H0
H16
30
30
10
10
3.333333
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0.184456
0.048
0.046968
0.157085
0.44135
0.105809
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0.184456
0.85038
0.002206
0.024577
0.033787
0.004707
0.000002
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7,647
train
H1
H16
30
30
10
6.666667
3.333333
0.762863
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0.811308
2.647558
1.141847
0.762863
0.777568
0.428564
0.10481
0.325445
0.603841
0.09026
0.128256
0.791651
7,647
train
H0
H16
50
50
10
12
6
-0.810479
0.792449
1.10737
1.108638
1.62944
3.102819
1.433557
1.108638
1.10737
0.376043
1.229077
0.774662
0.62575
1.563165
1.456476
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7,647
train
H1
H16
50
50
10
8
12
-0.306746
0.328199
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0.83435
0.727211
0.882216
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0.328199
0.643323
0.014983
0.022039
0.107402
0.275855
0.212774
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7,647
train
H0
H16
75
75
10
5.333333
13.333333
0.176558
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0.674901
0.68608
4.285172
4.568879
4.001051
0.68608
0.674901
0.135264
0.470706
0.011908
0.051146
2.427235
2.283804
0.231641
7,647
train
H1
H16
75
75
10
9.333333
9.333333
0.296828
-0.287484
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0.127496
0.468862
0.089675
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0.287484
0.772444
0.080122
0.115232
0.082431
0.019443
0.027639
0.28918
7,647
train
H0
H16
100
100
10
19
9
-2.19683
2.214822
1.628628
1.630597
5.312618
5.264069
5.577016
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2.214822
0.024985
2.658847
4.044622
4.919936
0.205074
0.232795
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7,647
train
H1
H16
100
100
10
14
6
-1.565172
1.535413
1.885086
1.89646
3.875524
5.811484
3.71441
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1.885086
0.121151
3.596562
2.749283
2.359173
3.449158
3.317763
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7,647
train
H0
H16
150
150
10
10.666667
13.333333
0.314213
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0.248944
0.249981
1.856982
2.513271
1.654295
0.314213
0.355384
0.437295
0.06249
0.020262
0.126204
0.778607
0.728741
0.366832
7,647
train
H1
H16
150
150
10
8.666667
10.666667
0.22084
-0.202318
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0.373005
1.063402
0.492501
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0.502056
0.781727
0.249733
0.133217
0.040897
0.421774
0.457086
0.205033
7,647
train
H0
H16
200
200
10
12
9.5
0.219383
-0.194379
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1.227134
3.961862
0.730259
0.219383
0.560148
0.265612
0.315123
0.054262
0.037763
0.650833
0.567017
0.196207
7,647
train
H1
H16
200
200
10
8
11
0.502884
-0.46605
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2.216986
2.467285
2.492732
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1.123242
0.287548
1.256142
0.595194
0.217079
2.278205
2.283406
0.47167
7,647
train
H0
H16
300
300
10
12
9
-0.628766
0.591201
1.148205
1.149395
2.569416
3.583074
2.134958
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1.148205
0.310146
1.321109
0.555007
0.349396
2.15778
2.065581
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7,647
train
H1
H16
300
300
10
10.333333
12.666667
-0.260894
0.347423
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10.690696
11.905363
9.500898
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1.092258
0.007714
1.208344
0.01876
0.120674
6.033262
5.888164
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7,647
train
H0
H16
500
500
10
8
9
1.010316
-0.970561
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3.449413
3.620464
3.285162
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1.583497
0.19348
2.514989
1.420622
0.942023
3.25874
3.217827
0.977559
7,647
train
H1
H16
500
500
10
10.6
8.6
-1.043206
1.066714
0.791445
0.79122
0.942926
3.339046
1.184662
0.79122
1.066714
0.283155
0.62603
0.913671
1.137864
0.08604
0.068703
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7,647
train
H0
H16
1,000
1,000
10
10.2
9.8
-1.32553
1.344511
0.820135
0.819707
2.810518
3.283358
2.397566
-1.32553
1.344511
0.17605
0.671919
1.519787
1.807883
0.000626
0.001949
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7,647
train
H1
H16
1,000
1,000
10
9.9
9.6
-0.276464
0.336474
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8.003576
7.3192
8.304744
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1.039298
0.015727
1.085695
0.050635
0.113237
5.15385
5.043191
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