8815440
1
Jan van Rijn
9954
Supervised Classification
7707
sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.svm.classes.SVC)(1)
6790490
axis
0
7660
categorical_features
[]
7660
copy
true
7660
fill_empty
0
7660
missing_values
"NaN"
7660
strategy
"median"
7660
strategy_nominal
"most_frequent"
7660
verbose
0
7660
categorical_features
[]
7661
dtype
{"oml-python:serialized_object": "type", "value": "np.float64"}
7661
handle_unknown
"ignore"
7661
n_values
"auto"
7661
sparse
true
7661
threshold
0.0
7662
copy
true
7663
with_mean
false
7663
with_std
true
7663
C
0.2435209206954405
7708
cache_size
200
7708
class_weight
null
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coef0
0.324936714172521
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decision_function_shape
null
7708
degree
4
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gamma
0.00838989053062089
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kernel
"sigmoid"
7708
max_iter
-1
7708
probability
true
7708
random_state
35403
7708
shrinking
true
7708
tol
1.485615585172483e-05
7708
verbose
false
7708
openml-pimp
openml-python
Sklearn_0.18.1.
study_71
1491
one-hundred-plants-margin
https://www.openml.org/data/download/1592283/phpCsX3fx
-1
18546798
description
https://api.openml.org/data/download/18546798/description.xml
-1
18546799
predictions
https://api.openml.org/data/download/18546799/predictions.arff
area_under_roc_curve
0.9239161142676768 [0.745778,0.946378,0.952296,0.996765,0.975103,0.97242,0.969381,0.945707,0.973564,1,0.957426,0.924874,0.874487,0.911024,0.983783,0.802399,0.955414,0.917337,0.930437,0.900765,0.943695,0.885693,0.892243,0.970052,0.943497,0.955887,0.868134,0.9055,0.945865,0.989347,0.930358,0.995068,0.842448,0.985164,0.863202,0.990885,0.957307,0.916588,0.980114,0.99491,0.767124,0.957189,0.996449,0.94547,0.986585,0.817669,0.981968,0.951468,0.892124,0.945865,0.848722,0.949574,0.945589,0.804885,0.939197,0.955492,0.999842,0.952375,0.954979,0.862492,0.936987,0.836924,0.986703,0.96291,0.98039,0.902423,0.859651,0.991872,0.940775,0.828954,0.826783,0.995778,0.871528,0.958531,0.861308,0.954782,0.979009,0.766769,0.988321,0.978259,0.892795,0.985204,0.930122,0.96512,0.824377,0.920297,0.999487,0.868253,0.821693,0.877407,0.906013,0.988202,0.974353,0.878157,0.896899,0.920099,0.857599,0.96003,0.814394,0.8561]
average_cost
0
kappa
0.17234848484848486
kb_relative_information_score
282.12574248784654
mean_absolute_error
0.01951918674280072
mean_prior_absolute_error
0.019800000000000036
number_of_instances
1600 [16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16]
predictive_accuracy
0.180625
prior_entropy
6.6438561897747395
recall
0.180625 [0,0,0.875,0.5625,0.1875,0.125,0.25,0.0625,0.3125,1,0.1875,0.0625,0,0,0.875,0,0.125,0,0,0,0,0.0625,0,0.1875,0.25,0,0,0,0.125,0.5,0.0625,0.9375,0,0,0,1,0,0,0.1875,0.8125,0,0.3125,0.5625,0,0.375,0,0.1875,0,0.125,0.0625,0,0.1875,0.25,0,0.1875,0,1,0.125,0.0625,0,0.125,0,0,0,0.4375,0,0,0.5625,0,0,0,1,0,0.25,0,0.0625,0.3125,0,0.375,0.5,0.0625,0,0.0625,0.0625,0,0.1875,1,0,0.0625,0,0.0625,0.5625,0.1875,0,0,0,0,0,0,0]
relative_absolute_error
0.9858175122626608
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.09832046738125778
root_relative_squared_error
0.9881578773212386
total_cost
0
area_under_roc_curve
0.9466973668497731 [0.691824,0.939873,0.981013,1,0.996835,1,0.990506,0.974843,0.993671,1,0.958861,0.936709,0.851266,0.914557,0.993671,0.748428,0.981013,0.946203,0.914557,0.855346,0.899371,0.981132,0.792453,0.968553,0.968553,0.96519,0.882911,0.996835,0.984177,0.981132,0.974684,1,0.879747,0.984177,0.927215,0.987421,0.974843,0.90566,0.987421,1,0.792453,0.968553,1,0.981013,1,0.835443,0.981132,0.981132,0.971519,0.936709,0.889241,0.987342,0.974843,0.917722,0.981132,0.993711,1,1,0.968553,0.920886,0.974843,0.886792,0.984177,0.987342,0.993711,0.96519,0.886076,1,1,0.842767,0.816456,1,0.914557,0.974684,0.962264,0.996835,0.993671,0.765823,1,0.984177,0.987342,0.993671,0.987421,0.962264,0.911392,0.974684,1,0.993671,0.829114,0.830189,0.943038,0.993711,0.996835,0.873418,0.971519,0.971519,0.811321,0.958861,0.882911,0.886792]
area_under_roc_curve
0.9455514588806624 [0.63522,0.977848,1,0.993711,1,1,0.993671,0.962264,0.984177,1,0.974684,0.968354,0.908228,0.740506,0.996835,0.805031,0.96519,0.949367,0.952532,0.867925,0.937107,0.849057,0.798742,0.993711,0.930818,0.949367,0.857595,1,0.977848,1,0.962025,0.993671,0.876582,0.993671,0.889241,0.993711,0.968553,0.90566,0.981132,1,0.767296,0.987421,1,0.958861,0.996835,0.898734,0.981132,0.955975,0.971519,0.977848,0.927215,0.962025,0.987421,0.917722,0.993711,0.987421,1,1,0.949686,0.917722,0.987421,0.836478,0.987342,0.984177,1,0.955696,0.876582,0.987421,0.949686,0.899371,0.832278,1,0.892405,0.971519,0.962264,0.990506,0.993671,0.78481,0.987342,1,0.96519,0.974684,0.974843,0.955975,0.927215,0.981013,1,0.987342,0.911392,0.918239,0.962025,1,0.993671,0.832278,0.968354,0.952532,0.823899,0.955696,0.806962,0.930818]
area_under_roc_curve
0.947734057797946 [0.867089,0.968354,0.981013,0.993711,0.987342,0.987342,0.90566,0.939873,0.977848,1,0.984177,0.962025,0.893082,0.977848,0.974684,0.85443,1,0.882911,0.937107,0.96519,0.874214,0.767296,0.832278,0.968553,0.899371,0.968354,0.867089,0.943396,0.993671,0.996835,0.899371,1,0.863924,0.981013,0.792453,1,0.955975,0.91195,0.974843,1,0.832278,0.955975,1,0.993711,0.990506,0.81761,0.993671,0.943396,0.971519,0.990506,0.949367,0.974843,0.974843,0.962025,1,0.977848,1,0.993671,0.918239,0.958861,0.981132,0.918239,0.996835,0.962264,0.993671,0.958861,0.886076,1,0.943396,0.939873,0.89557,0.990506,0.90566,0.987342,0.937107,0.990506,0.987342,0.832278,0.981013,0.968354,0.943396,0.977848,0.987342,0.962025,0.81761,0.949686,1,0.974684,0.857595,0.842767,0.914557,0.968553,1,0.898734,0.939873,0.91195,0.905063,0.984177,0.90566,0.933544]
area_under_roc_curve
0.9474802961547646 [0.759494,0.958861,0.971519,1,0.984177,0.962025,0.962264,0.962025,0.971519,1,0.984177,0.952532,0.867925,0.911392,0.984177,0.848101,0.996835,0.920886,0.849057,0.914557,0.968553,0.955975,0.968354,0.993711,0.968553,0.952532,0.860759,0.893082,1,1,0.930818,0.993671,0.860759,0.993671,0.874214,0.993711,0.949686,0.930818,0.981132,1,0.901899,1,1,0.993711,0.996835,0.811321,0.981013,0.974843,0.993671,0.996835,0.971519,0.974843,0.968553,0.886076,0.981132,0.949367,1,0.993671,0.943396,0.952532,0.974843,0.91195,0.993671,0.981132,0.996835,0.962025,0.886076,0.984177,0.993711,0.85443,0.844937,0.996835,0.81761,0.984177,0.842767,0.993671,0.984177,0.75,0.993671,0.987342,0.949686,0.974684,0.96519,0.971519,0.993711,0.993711,1,0.968354,0.924051,0.874214,0.93038,1,0.981132,0.914557,0.977848,0.91195,0.848101,0.949367,0.704403,0.977848]
area_under_roc_curve
0.9440900903590478 [0.892405,0.955975,0.981013,1,0.987421,0.987342,0.974843,0.96519,0.968553,1,0.962025,0.96519,0.867925,0.918239,0.981132,0.816456,0.993671,0.943038,0.880503,0.892405,0.958861,0.936709,0.996835,0.974684,0.958861,0.949367,0.870253,1,0.981132,0.993671,0.949686,1,0.800633,0.977848,0.786164,1,0.952532,0.901899,0.984177,1,0.759494,0.993671,1,0.949686,0.993711,0.72327,0.993671,0.981013,0.993671,0.977848,0.918239,0.981132,0.996835,0.962264,0.996835,0.981013,1,0.971519,0.962025,0.943396,0.987342,0.851266,0.974843,1,1,0.984177,0.930818,1,0.974684,0.949367,0.825949,1,0.867925,0.974843,0.806962,0.955975,0.981013,0.765823,1,0.974843,0.949686,0.987342,0.984177,0.962025,0.867925,0.867925,1,0.955975,0.898734,0.933544,0.867925,0.984177,1,0.867925,0.924051,0.893082,0.93038,0.981132,0.748428,0.832278]
area_under_roc_curve
0.9420194451078736 [0.78481,0.962264,0.958861,1,1,0.936709,0.968553,0.946203,1,1,0.981013,0.936709,0.861635,0.968553,0.981132,0.832278,0.971519,0.920886,0.930818,0.863924,0.939873,0.863924,0.996835,0.987342,0.939873,0.949367,0.867089,0.893082,0.981132,0.996835,0.937107,1,0.892405,0.993671,0.72327,0.993671,0.968354,0.911392,0.984177,1,0.737342,0.984177,1,0.968553,0.987421,0.861635,1,0.943038,0.974684,0.987342,0.918239,0.993711,0.984177,0.893082,0.962025,0.943038,1,0.993671,0.968354,0.937107,0.974684,0.851266,0.981132,0.993711,1,0.958861,0.867925,1,0.987342,0.917722,0.851266,0.987342,0.874214,0.993711,0.911392,0.955975,0.990506,0.718354,0.993671,0.955975,0.949686,1,0.977848,0.955696,0.842767,0.974843,1,0.974843,0.908228,0.943038,0.968553,0.987342,0.974843,0.949686,0.949367,0.930818,0.876582,0.993711,0.798742,0.851266]
area_under_roc_curve
0.9453959676777333 [0.844937,0.955975,0.981132,1,1,1,0.977848,0.955696,0.987421,1,0.968553,0.949686,0.905063,0.987421,0.987421,0.759494,0.943396,0.830189,0.958861,0.857595,0.987342,0.949367,0.93038,0.968354,0.981013,0.886792,0.91195,0.949367,0.993711,0.987342,0.949367,0.993711,0.805031,0.968553,0.908228,1,0.958861,0.927215,0.996835,1,0.759494,0.987342,1,0.987342,0.949686,0.939873,0.958861,0.927215,0.968553,0.987421,0.886792,0.987342,0.974684,0.830189,0.962025,0.949367,1,0.990506,0.962025,0.987421,1,0.78481,0.987421,0.990506,1,0.886792,0.874214,0.993671,0.971519,0.962025,0.805031,1,0.889241,1,0.917722,0.993711,1,0.685535,1,0.981132,0.901899,1,0.977848,0.971519,0.946203,0.981013,1,0.943396,0.811321,0.879747,0.924528,1,0.990506,0.842767,0.949686,0.977848,0.867089,0.943396,0.863924,0.93038]
area_under_roc_curve
0.9432820336756624 [0.863924,0.930818,0.987421,1,0.993711,0.993671,0.974684,0.968354,0.949686,1,0.943396,0.949686,0.882911,0.993711,0.981132,0.810127,0.930818,0.823899,0.933544,0.936709,0.96519,0.939873,0.905063,0.974684,0.968354,0.962264,0.867925,0.981013,0.842767,0.987342,0.952532,1,0.849057,0.993711,0.89557,1,0.949367,0.920886,0.987342,1,0.810127,0.990506,1,0.977848,0.981132,0.892405,1,0.96519,0.924528,0.918239,0.710692,0.962025,0.962025,0.91195,0.987342,0.943038,1,0.990506,0.962025,0.811321,0.987342,0.822785,0.987421,0.993671,1,0.761006,0.779874,1,0.984177,0.996835,0.798742,0.993671,0.911392,0.968553,0.863924,0.981132,1,0.823899,0.993711,0.981132,0.958861,0.993711,0.962025,0.974684,0.933544,0.981013,1,0.949686,0.773585,0.901899,0.955975,1,1,0.90566,0.949686,0.917722,0.908228,0.968553,0.857595,0.863924]
area_under_roc_curve
0.949284242894674 [0.811321,0.93038,0.962264,0.996835,1,0.981132,0.977848,0.974843,0.977848,1,0.962264,0.943396,0.89557,0.962025,0.993671,0.805031,0.943396,0.918239,0.952532,0.880503,0.971519,0.927215,0.874214,0.974684,0.96519,0.974843,0.867925,0.990506,1,1,0.952532,0.996835,0.849057,0.987421,0.933544,1,0.962025,0.955696,0.987342,1,0.704403,1,1,0.96519,0.996835,0.787975,1,0.981013,0.955975,0.962264,0.898734,0.981013,0.987342,0.879747,0.974684,0.91195,1,0.981132,0.977848,0.981013,0.990506,0.879747,1,0.996835,0.993711,0.968553,0.85443,1,0.987342,0.899371,0.811321,0.987421,0.829114,1,0.908228,0.968354,0.993711,0.786164,0.987421,0.981013,0.971519,0.981132,0.987421,0.974843,0.958861,0.984177,1,0.974684,0.754717,0.867089,0.927215,0.990506,0.987342,0.892405,0.937107,0.879747,0.91195,1,0.851266,0.867925]
area_under_roc_curve
0.9468354430379744 [0.786164,0.93038,0.993711,1,1,1,0.993671,0.962264,0.974684,1,0.949686,0.943396,0.876582,0.952532,0.990506,0.748428,0.924528,0.968553,0.943038,0.867925,0.914557,0.867089,0.91195,0.971519,0.946203,0.981132,0.855346,0.933544,0.987342,0.981132,0.984177,1,0.805031,1,0.952532,0.993671,0.974684,0.936709,0.987342,1,0.830189,0.990506,1,0.984177,0.987342,0.946203,1,0.96519,0.930818,0.987421,0.955696,0.984177,0.962025,0.892405,0.996835,0.962264,1,0.993711,0.968354,0.933544,0.990506,0.908228,0.984177,0.990506,0.987421,0.955975,0.882911,0.993711,1,0.874214,0.798742,1,0.952532,0.96519,0.85443,0.968354,1,0.748428,0.987421,0.990506,0.981013,0.968553,0.968553,0.993711,0.905063,0.968354,1,0.920886,0.773585,0.85443,0.901899,1,0.993671,0.876582,0.993711,0.933544,0.786164,0.949367,0.797468,0.786164]
average_cost
0
average_cost
0
average_cost
0
average_cost
0
average_cost
0
average_cost
0
average_cost
0
average_cost
0
average_cost
0
average_cost
0
kappa
0.14394744089067232
kappa
0.18804091266719122
kappa
0.17506297229219142
kappa
0.16866758512103913
kappa
0.15550653852213647
kappa
0.16824196597353497
kappa
0.18113460099996062
kappa
0.18765743073047858
kappa
0.18778529828427515
kappa
0.1876254575510686
kb_relative_information_score
27.962794077333175
kb_relative_information_score
27.808795188050553
kb_relative_information_score
28.454383726687507
kb_relative_information_score
28.31421852823139
kb_relative_information_score
28.34826754934022
kb_relative_information_score
28.110484725250398
kb_relative_information_score
28.319069009498502
kb_relative_information_score
28.324894804510098
kb_relative_information_score
28.28140696878331
kb_relative_information_score
28.20142791016198
mean_absolute_error
0.01952274265228484
mean_absolute_error
0.019525271997755982
mean_absolute_error
0.019512442123543965
mean_absolute_error
0.01951669583076033
mean_absolute_error
0.01951804231139196
mean_absolute_error
0.019521591211899753
mean_absolute_error
0.019517212681141826
mean_absolute_error
0.01951708108359918
mean_absolute_error
0.019520249044855498
mean_absolute_error
0.019520538490773565
mean_prior_absolute_error
0.019800000000000033
mean_prior_absolute_error
0.019800000000000033
mean_prior_absolute_error
0.019800000000000033
mean_prior_absolute_error
0.019800000000000033
mean_prior_absolute_error
0.019800000000000033
mean_prior_absolute_error
0.019800000000000033
mean_prior_absolute_error
0.019800000000000033
mean_prior_absolute_error
0.019800000000000033
mean_prior_absolute_error
0.019800000000000033
mean_prior_absolute_error
0.019800000000000033
number_of_instances
160 [1,2,2,1,2,1,2,1,2,1,2,2,2,2,2,1,2,2,2,1,1,1,1,1,1,2,2,2,2,1,2,2,2,2,2,1,1,1,1,2,1,1,2,2,2,2,1,1,2,2,2,2,1,2,1,1,2,1,1,2,1,1,2,2,1,2,2,1,1,1,2,1,2,2,1,2,2,2,2,2,2,2,1,1,2,2,2,2,2,1,2,1,2,2,2,2,1,2,2,1]
number_of_instances
160 [1,2,2,1,2,1,2,1,2,1,2,2,2,2,2,1,2,2,2,1,1,1,1,1,1,2,2,2,2,1,2,2,2,2,2,1,1,1,1,2,1,1,2,2,2,2,1,1,2,2,2,2,1,2,1,1,2,1,1,2,1,1,2,2,1,2,2,1,1,1,2,1,2,2,1,2,2,2,2,2,2,2,1,1,2,2,2,2,2,1,2,1,2,2,2,2,1,2,2,1]
number_of_instances
160 [2,2,2,1,2,2,1,2,2,2,2,2,1,2,2,2,2,2,1,2,1,1,2,1,1,2,2,1,2,2,1,2,2,2,1,1,1,1,1,2,2,1,1,1,2,1,2,1,2,2,2,1,1,2,1,2,1,2,1,2,1,1,2,1,2,2,2,2,1,2,2,2,1,2,1,2,2,2,2,2,1,2,2,2,1,1,1,2,2,1,2,1,1,2,2,1,2,2,1,2]
number_of_instances
160 [2,2,2,1,2,2,1,2,2,2,2,2,1,2,2,2,2,2,1,2,1,1,2,1,1,2,2,1,2,2,1,2,2,2,1,1,1,1,1,2,2,1,1,1,2,1,2,1,2,2,2,1,1,2,1,2,1,2,1,2,1,1,2,1,2,2,2,2,1,2,2,2,1,2,1,2,2,2,2,2,1,2,2,2,1,1,1,2,2,1,2,1,1,2,2,1,2,2,1,2]
number_of_instances
160 [2,1,2,2,1,2,1,2,1,2,2,2,1,1,1,2,2,2,1,2,2,2,2,2,2,2,2,1,1,2,1,1,2,2,1,2,2,2,2,2,2,2,1,1,1,1,2,2,2,2,1,1,2,1,2,2,1,2,2,1,2,2,1,1,2,2,1,2,2,2,2,2,1,1,2,1,2,2,2,1,1,2,2,2,1,1,1,1,2,2,1,2,1,1,2,1,2,1,1,2]
number_of_instances
160 [2,1,2,2,1,2,1,2,1,2,2,2,1,1,1,2,2,2,1,2,2,2,2,2,2,2,2,1,1,2,1,1,2,2,1,2,2,2,2,2,2,2,1,1,1,1,2,2,2,2,1,1,2,1,2,2,1,2,2,1,2,2,1,1,2,2,1,2,2,2,2,2,1,1,2,1,2,2,2,1,1,2,2,2,1,1,1,1,2,2,1,2,1,1,2,1,2,1,1,2]
number_of_instances
160 [2,1,1,2,1,2,2,2,1,2,1,1,2,1,1,2,1,1,2,2,2,2,2,2,2,1,1,2,1,2,2,1,1,1,2,2,2,2,2,1,2,2,2,2,1,2,2,2,1,1,1,2,2,1,2,2,2,2,2,1,2,2,1,2,2,1,1,2,2,2,1,2,2,1,2,1,1,1,1,1,2,1,2,2,2,2,2,1,1,2,1,2,2,1,1,2,2,1,2,2]
number_of_instances
160 [2,1,1,2,1,2,2,2,1,2,1,1,2,1,1,2,1,1,2,2,2,2,2,2,2,1,1,2,1,2,2,1,1,1,2,2,2,2,2,1,2,2,2,2,1,2,2,2,1,1,1,2,2,1,2,2,2,2,2,1,2,2,1,2,2,1,1,2,2,2,1,2,2,1,2,1,1,1,1,1,2,1,2,2,2,2,2,1,1,2,1,2,2,1,1,2,2,1,2,2]
number_of_instances
160 [1,2,1,2,2,1,2,1,2,1,1,1,2,2,2,1,1,1,2,1,2,2,1,2,2,1,1,2,2,1,2,2,1,1,2,2,2,2,2,1,1,2,2,2,2,2,1,2,1,1,2,2,2,2,2,1,2,1,2,2,2,2,2,2,1,1,2,1,2,1,1,1,2,2,2,2,1,1,1,2,2,1,1,1,2,2,2,2,1,2,2,2,2,2,1,2,1,2,2,1]
number_of_instances
160 [1,2,1,2,2,1,2,1,2,1,1,1,2,2,2,1,1,1,2,1,2,2,1,2,2,1,1,2,2,1,2,2,1,1,2,2,2,2,2,1,1,2,2,2,2,2,1,2,1,1,2,2,2,2,2,1,2,1,2,2,2,2,2,2,1,1,2,1,2,1,1,1,2,2,2,2,1,1,1,2,2,1,1,1,2,2,2,2,1,2,2,2,2,2,1,2,1,2,2,1]
predictive_accuracy
0.15
predictive_accuracy
0.19375
predictive_accuracy
0.18125
predictive_accuracy
0.175
predictive_accuracy
0.1625
predictive_accuracy
0.175
predictive_accuracy
0.1875
predictive_accuracy
0.19375
predictive_accuracy
0.19375
predictive_accuracy
0.19375
prior_entropy
6.6438561897747395
prior_entropy
6.6438561897747395
prior_entropy
6.6438561897747395
prior_entropy
6.6438561897747395
prior_entropy
6.6438561897747395
prior_entropy
6.6438561897747395
prior_entropy
6.6438561897747395
prior_entropy
6.6438561897747395
prior_entropy
6.6438561897747395
prior_entropy
6.6438561897747395
recall
0.15 [0,0,1,1,0,0,0,0,0,1,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0.5,0,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0]
recall
0.19375 [0,0,1,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,1,0,0,0,1,0,0,1,0.5,0,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,0,1,0,0.5,0,0,0.5,0,0.5,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0]
recall
0.18125 [0,0,0.5,1,0,0,1,0,0,1,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,1,0,1,1,0,0.5,0,0,0,0,0,0,1,1,0,1,0,1,0,1,0,0,0,0,0,0.5,0,0,0.5,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0]
recall
0.175 [0,0,0.5,1,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0.5,0,0,0,0,0,1,1,0,0,0,0,1,1,0,0,0,0,0,0,0]
recall
0.1625 [0,0,1,0.5,1,0,1,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.5,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,1,0,0,0,0,0.5,0,0,1,1,0,0,0,0,0,1,0,0,0,1,1,1,0,0,0,0,0,0,0]
recall
0.175 [0,0,1,0,0,0,1,0,1,1,0,0,0,0,1,0,0.5,0,0,0,0,0,0,0,0.5,0,0,0,0,0,1,1,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,1,1,0,0,0,0,0,0,0]
recall
0.1875 [0,0,1,0,1,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.5,0,0,0.5,0,0,0,1,0,1,0,0,1,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0.5,0,0,0,0,0,0,0,0]
recall
0.19375 [0,0,1,0,0,0,0,0,1,1,1,1,0,0,1,0,1,0,0,0,0,0,0,0.5,0.5,0,0,0,1,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0]
recall
0.19375 [0,0,1,1,0,1,0,1,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0.5,0.5,0,0.5,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]
recall
0.19375 [0,0,1,1,0.5,1,0,0,0.5,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0.5,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0.5,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]
relative_absolute_error
0.9859971036507479
relative_absolute_error
0.9861248483715126
relative_absolute_error
0.9854768749264612
relative_absolute_error
0.9856917086242574
relative_absolute_error
0.985759712696562
relative_absolute_error
0.9859389500959455
relative_absolute_error
0.9857178121788784
relative_absolute_error
0.9857111658383407
relative_absolute_error
0.9858711638815891
relative_absolute_error
0.9858857823622996
root_mean_prior_squared_error
0.09949874371066206
root_mean_prior_squared_error
0.09949874371066206
root_mean_prior_squared_error
0.09949874371066206
root_mean_prior_squared_error
0.09949874371066206
root_mean_prior_squared_error
0.09949874371066206
root_mean_prior_squared_error
0.09949874371066206
root_mean_prior_squared_error
0.09949874371066206
root_mean_prior_squared_error
0.09949874371066206
root_mean_prior_squared_error
0.09949874371066206
root_mean_prior_squared_error
0.09949874371066206
root_mean_squared_error
0.09834241708130377
root_mean_squared_error
0.09834367542532507
root_mean_squared_error
0.09828777502681385
root_mean_squared_error
0.09830685419459574
root_mean_squared_error
0.09831531060254124
root_mean_squared_error
0.0983277800052186
root_mean_squared_error
0.09831449006617536
root_mean_squared_error
0.09831372562651006
root_mean_squared_error
0.09832482116336247
root_mean_squared_error
0.0983278118631909
root_relative_squared_error
0.9883784801070371
root_relative_squared_error
0.9883911269402972
root_relative_squared_error
0.9878293067963787
root_relative_squared_error
0.9880210596474235
root_relative_squared_error
0.9881060497450883
root_relative_squared_error
0.9882313719573328
root_relative_squared_error
0.9880978030443234
root_relative_squared_error
0.988090120136612
root_relative_squared_error
0.9882016344777849
root_relative_squared_error
0.9882316921420015
total_cost
0
total_cost
0
total_cost
0
total_cost
0
total_cost
0
total_cost
0
total_cost
0
total_cost
0
total_cost
0
total_cost
0
usercpu_time_millis
2866.4319460003753
usercpu_time_millis
2633.869044000676
usercpu_time_millis
2636.666028000036
usercpu_time_millis
2667.4926660016354
usercpu_time_millis
2649.8572919990693
usercpu_time_millis
2665.7187979981245
usercpu_time_millis
2651.689995000197
usercpu_time_millis
2652.8184250000777
usercpu_time_millis
2651.5235619990563
usercpu_time_millis
2645.9404960005486
usercpu_time_millis_testing
151.37042499918607
usercpu_time_millis_testing
150.80420400045114
usercpu_time_millis_testing
154.78071000143245
usercpu_time_millis_testing
160.14444500069658
usercpu_time_millis_testing
150.61143499951868
usercpu_time_millis_testing
150.55005899921525
usercpu_time_millis_testing
150.11812500051747
usercpu_time_millis_testing
150.25411399983568
usercpu_time_millis_testing
150.04112899987376
usercpu_time_millis_testing
150.1185479992273
usercpu_time_millis_training
2715.0615210011892
usercpu_time_millis_training
2483.0648400002246
usercpu_time_millis_training
2481.8853179986036
usercpu_time_millis_training
2507.348221000939
usercpu_time_millis_training
2499.2458569995506
usercpu_time_millis_training
2515.1687389989092
usercpu_time_millis_training
2501.5718699996796
usercpu_time_millis_training
2502.564311000242
usercpu_time_millis_training
2501.4824329991825
usercpu_time_millis_training
2495.8219480013213