53943
4221
{"search_strategy":"beam","use_nominal_sets":"false","post_processing_count":"20","maximum_coverage_fraction":"1.0","numeric_operators":"=","overall_ranking_loss":"0.0","post_processing_do_autorun":"true","search_depth":"4","search_strategy_width":"1024","nr_threads":"1","alpha":"0.5","beam_seed":"","maximum_subgroups":"100","maximum_time":"1.0","minimum_coverage":"2","nr_bins":"64","numeric_strategy":"bins","beta":"1.0"}
search_strategy
beam
4221
use_nominal_sets
false
4221
post_processing_count
20
4221
maximum_coverage_fraction
1.0
4221
numeric_operators
=
4221
overall_ranking_loss
0.0
4221
post_processing_do_autorun
true
4221
search_depth
4
4221
search_strategy_width
1024
4221
nr_threads
1
4221
alpha
0.5
4221
beam_seed
4221
maximum_subgroups
100
4221
maximum_time
1.0
4221
minimum_coverage
2
4221
nr_bins
64
4221
numeric_strategy
bins
4221
beta
1.0
4221
Cortana
coverage
openml.evaluation.coverage(1.0)
688
[688, 300, 491, 275, 337, 240, 204, 296, 664, 538, 412, 325, 533, 727, 146, 564, 287, 69, 30, 223, 95, 103, 208, 314, 343, 246, 253, 257, 404, 143, 377, 19, 75, 102, 110, 7, 15, 406, 304, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 264, 5, 5, 5, 5, 5, 6, 6, 7, 85, 111, 321, 10, 10, 11, 11, 12, 328, 124, 126, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
quality
openml.evaluation.quality(1.0)
0.2167680412530899
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probability
openml.evaluation.probability(1.0)
0.12936046511627908
[0.12936046511627908, 0.24, 0.14052953156822812, 0.20727272727272728, 0.17507418397626112, 0.20416666666666666, 0.19607843137254902, 0.14189189189189189, 0.08433734939759036, 0.09293680297397769, 0.10679611650485436, 0.12, 0.07692307692307693, 0.06602475928473177, 0.15753424657534246, 0.06914893617021277, 0.0975609756097561, 0.21739130434782608, 0.4, 0.08071748878923767, 0.1368421052631579, 0.1262135922330097, 0.08173076923076923, 0.06369426751592357, 0.061224489795918366, 0.06910569105691057, 0.06719367588932806, 0.06614785992217899, 0.054455445544554455, 0.07692307692307693, 0.050397877984084884, 0.2631578947368421, 0.09333333333333334, 0.0784313725490196, 0.07272727272727272, 0.5714285714285714, 0.26666666666666666, 0.046798029556650245, 0.049342105263157895, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.75, 0.75, 0.75, 0.75, 0.04924242424242424, 0.6, 0.6, 0.6, 0.6, 0.6, 0.5, 0.5, 0.42857142857142855, 0.07058823529411765, 0.06306306306306306, 0.04672897196261682, 0.3, 0.3, 0.2727272727272727, 0.2727272727272727, 0.25, 0.04573170731707317, 0.056451612903225805, 0.05555555555555555, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
positives
openml.evaluation.positives(1.0)
89
[89, 72, 69, 57, 59, 49, 40, 42, 56, 50, 44, 39, 41, 48, 23, 39, 28, 15, 12, 18, 13, 13, 17, 20, 21, 17, 17, 17, 22, 11, 19, 5, 7, 8, 8, 4, 4, 19, 15, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 13, 3, 3, 3, 3, 3, 3, 3, 3, 6, 7, 15, 3, 3, 3, 3, 3, 15, 7, 7, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
cortana_quality
openml.evaluation.cortana_quality(1.0)
0.2167680412530899
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