4980581
1
Jan van Rijn
9954
Supervised Classification
6970
sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier))(1)
3015978
class_weight
null
6941
criterion
"gini"
6941
max_depth
4
6941
max_features
null
6941
max_leaf_nodes
null
6941
min_impurity_split
1e-07
6941
min_samples_leaf
1
6941
min_samples_split
2
6941
min_weight_fraction_leaf
0.0
6941
presort
false
6941
random_state
21866
6941
splitter
"best"
6941
axis
0
6947
categorical_features
[]
6947
copy
true
6947
fill_empty
0
6947
missing_values
"NaN"
6947
strategy
"median"
6947
strategy_nominal
"most_frequent"
6947
verbose
0
6947
categorical_features
[]
6948
dtype
{"oml-python:serialized_object": "type", "value": "np.float64"}
6948
handle_unknown
"ignore"
6948
n_values
"auto"
6948
sparse
false
6948
threshold
0.0
6949
algorithm
"SAMME.R"
6971
learning_rate
0.8198598193792997
6971
n_estimators
82
6971
random_state
14443
6971
openml-pimp
openml-python
Sklearn_0.18.1.
study_71
1491
one-hundred-plants-margin
https://www.openml.org/data/download/1592283/phpCsX3fx
-1
10895049
description
https://api.openml.org/data/download/10895049/description.xml
-1
10895050
predictions
https://api.openml.org/data/download/10895050/predictions.arff
area_under_roc_curve
0.9059055397727275 [0.771327,0.983033,0.975024,1,0.914891,0.96295,0.949179,0.979285,0.98402,1,0.991004,0.963857,0.942985,0.887251,0.974235,0.899641,0.946575,0.929944,0.913786,0.917949,0.941091,0.90988,0.813269,0.995975,0.932213,0.89755,0.857955,0.806838,0.863518,0.945983,0.917594,0.972972,0.916134,0.924775,0.749882,0.959557,0.925308,0.919113,0.991714,0.936553,0.827099,0.792337,0.942294,0.880465,0.982797,0.883779,0.997317,0.959359,0.822857,0.812046,0.914536,0.885338,0.933416,0.86847,0.796875,0.908953,1,0.832268,0.970407,0.867049,0.883542,0.879577,0.932884,0.838009,0.9215,0.900233,0.822305,0.991635,0.69693,0.854186,0.80522,0.994792,0.873599,0.891631,0.823035,0.874428,0.876795,0.779257,0.9942,0.997672,0.868509,0.988755,0.766434,0.988676,0.856574,0.827099,0.997633,0.879991,0.873007,0.935705,0.930536,0.996567,0.847656,0.964765,0.864761,0.94474,0.894472,0.974235,0.775036,0.841501]
average_cost
0
kappa
0.2342171717171717
kb_relative_information_score
539.1805031295829
mean_absolute_error
0.01818768061811349
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
0.241875
prior_entropy
6.6438561897747395
recall
0.241875 [0,0.1875,0.625,0.5625,0.3125,0.3125,0.25,0.5,0.375,0.6875,0.4375,0.5625,0.125,0.375,0.625,0.25,0.25,0,0.0625,0,0,0.25,0.125,0.75,0.3125,0,0.0625,0.0625,0.0625,0.3125,0.375,0.5625,0.125,0,0.0625,0.0625,0,0,0.3125,0.375,0.125,0.125,0.4375,0.0625,0.25,0.125,0.3125,0.25,0.25,0,0.125,0.125,0.125,0.0625,0.0625,0,0.9375,0.125,0.75,0,0,0.1875,0.5625,0.125,0.25,0.125,0.0625,0.625,0.0625,0,0.25,0.3125,0.1875,0.375,0.1875,0.125,0.125,0.0625,0.8125,0.625,0.1875,0.4375,0,0.4375,0.0625,0.25,0.6875,0.0625,0.25,0.1875,0.0625,0.375,0.0625,0.1875,0.25,0.3125,0.125,0.375,0.125,0.125]
relative_absolute_error
0.9185697281875482
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.094506250133254
root_relative_squared_error
0.9498235516226613
total_cost
0
area_under_roc_curve
0.9181978196401556 [0.871069,0.977848,1,1,0.933544,1,1,1,1,1,0.984177,0.905063,0.987342,0.727848,1,0.701258,0.993671,0.974684,0.936709,0.893082,0.968553,1,0.528302,1,1,0.893987,0.935127,0.911392,0.742089,0.886792,0.879747,0.962025,0.962025,0.908228,0.901899,0.974843,0.974843,0.908805,1,0.971519,0.924528,0.943396,1,0.838608,1,0.813291,1,0.993711,0.905063,0.528481,0.985759,0.996835,0.993711,0.898734,0.748428,0.924528,1,0.937107,0.974843,0.761076,0.937107,0.754717,1,0.943038,1,0.946203,0.968354,1,0.896226,0.940252,0.768987,0.981132,0.927215,0.939873,0.974843,0.931962,0.871835,0.759494,1,1,0.897152,1,0.874214,1,0.911392,0.821203,0.993671,0.892405,0.719937,0.496855,0.950949,1,0.946203,0.974684,0.971519,0.946203,0.968553,0.971519,0.705696,0.987421]
area_under_roc_curve
0.9322018549478541 [0.877358,1,0.987342,1,0.998418,1,1,0.993711,0.996835,1,0.984177,0.990506,0.933544,0.905063,1,0.974843,0.914557,0.985759,0.897152,0.959119,0.977987,0.924528,0.77044,1,0.937107,0.882911,0.938291,0.81962,0.835443,0.987421,0.884494,0.893987,1,0.924051,0.658228,0.987421,0.871069,0.940252,0.987421,0.976266,0.946541,0.95283,0.917722,0.905063,0.996835,0.922468,1,0.974843,0.952532,0.892405,0.947785,0.670886,0.996855,0.947785,0.927673,0.987421,1,0.880503,0.993711,0.892405,0.902516,0.993711,1,0.924051,1,0.955696,0.905063,1,0.701258,0.783019,0.988924,1,0.906646,0.957278,0.993711,0.860759,0.852848,0.919304,1,1,0.931962,0.990506,0.814465,0.962264,0.928797,0.824367,1,0.860759,0.993671,0.968553,0.898734,1,0.955696,0.933544,0.966772,0.886076,0.981132,0.968354,0.745253,0.987421]
area_under_roc_curve
0.938671284133429 [0.876582,0.990506,0.968354,1,0.977848,0.996835,0.981132,0.974684,0.946203,1,0.990506,0.990506,0.937107,0.996835,1,0.987342,1,0.924051,0.918239,0.914557,0.993711,0.968553,0.941456,1,0.987421,0.96519,0.911392,0.899371,0.889241,1,1,1,0.901899,0.968354,0.95283,0.993711,0.943396,0.783019,0.981132,0.935127,0.890823,0.559748,1,0.880503,0.987342,0.930818,0.990506,0.974843,0.841772,0.946203,0.806962,1,0.993711,0.844937,1,0.974684,1,0.901899,0.91195,0.933544,0.921384,0.993711,1,0.767296,0.977848,0.848101,0.867089,1,0.691824,0.89557,0.762658,0.990506,0.871069,0.952532,0.899371,0.917722,0.860759,0.878165,0.990506,1,0.993711,0.996835,0.938291,1,0.811321,1,1,0.927215,0.939873,0.981132,0.96519,1,0.993711,0.987342,0.927215,0.974843,0.889241,0.971519,0.899371,0.748418]
area_under_roc_curve
0.8923092807101343 [0.841772,1,1,1,0.974684,0.981013,0.974843,0.984177,0.987342,1,0.996835,1,0.90566,0.996835,0.993671,0.990506,0.838608,0.914557,0.849057,0.829114,0.90566,1,0.901899,1,0.91195,0.914557,0.781646,0.86478,0.879747,0.879747,1,1,0.664557,0.914557,0.361635,0.91195,0.91195,0.811321,1,0.873418,0.648734,0.490566,0.993711,0.852201,0.987342,0.833333,1,0.993711,0.615506,0.718354,0.873418,0.845912,0.845912,0.879747,0.86478,0.914557,1,0.81962,0.974843,0.879747,0.641509,0.194969,0.990506,0.852201,0.879747,0.756329,0.901899,0.93038,0.676101,0.925633,0.882911,0.996835,0.880503,0.829114,0.779874,0.873418,0.873418,0.786392,1,0.993671,0.877358,0.977848,0.77057,0.987342,0.710692,0.962264,1,0.879747,0.935127,0.924528,0.914557,1,0.981132,0.984177,0.852848,0.974843,0.939873,1,0.861635,0.835443]
area_under_roc_curve
0.9124921632433726 [0.816456,0.937107,0.993671,1,1,0.981013,0.993711,1,1,1,0.981013,0.974684,0.987421,0.396226,1,0.920886,0.800633,0.832278,0.981132,0.901899,0.988924,0.996835,0.943038,0.987342,0.908228,0.908228,0.906646,0.845912,0.902516,0.93038,0.698113,1,0.838608,0.916139,0.959119,0.952532,0.927215,0.781646,0.990506,0.990506,0.851266,0.914557,0.921384,0.927673,1,0.889937,1,0.993671,0.981013,0.93038,0.987421,0.855346,0.954114,0.754717,0.609177,0.800633,1,1,1,0.943396,0.981013,0.816456,0.974843,0.852201,0.947785,0.908228,0.924528,0.996835,0.681962,0.919304,0.851266,1,0.622642,0.981132,0.791139,0.993711,0.881329,0.901899,1,1,0.833333,0.996835,0.743671,1,0.981132,0.63522,1,0.908805,0.816456,0.981013,0.924528,0.981013,0.899371,0.949686,0.613924,0.90566,0.962025,0.987421,0.842767,0.924051]
area_under_roc_curve
0.9144777237083034 [0.765823,0.993711,0.933544,1,1,0.901899,0.924528,0.977848,1,1,1,0.974684,0.959119,0.852201,1,0.952532,0.96519,0.914557,0.91195,0.927215,0.927215,0.818038,0.802215,1,0.990506,0.908228,0.792722,0.688679,0.915094,0.925633,1,0.933962,0.996835,0.876582,0.927673,0.952532,0.901899,0.933544,0.984177,0.93038,0.696203,0.917722,0.81761,0.915094,0.974843,1,1,0.917722,0.813291,0.892405,1,1,0.849684,0.915094,0.765823,0.867089,1,0.867089,1,0.701258,0.838608,0.981013,0.993711,0.915094,0.917722,0.93038,0.380503,1,0.84019,0.844937,0.700949,0.996835,0.981132,1,0.996835,0.908805,0.917722,0.556962,1,1,0.974843,0.984177,0.93038,0.990506,0.915094,0.955975,1,0.764151,0.892405,0.969937,0.993711,1,0.915094,0.993711,0.924051,1,0.996835,0.987421,0.893082,0.80538]
area_under_roc_curve
0.9082568316614921 [0.78481,1,0.955975,1,1,0.993671,0.946203,0.987342,0.993711,1,1,1,0.89557,0.974843,1,0.841772,0.993711,0.91195,0.848101,0.96519,0.920886,0.718354,0.810127,1,0.90981,0.710692,0.899371,0.778481,0.845912,1,0.996835,1,0.981132,0.943396,0.955696,0.993671,0.898734,0.993671,0.993671,1,0.958861,0.699367,0.867089,0.867089,0.823899,0.911392,0.990506,0.939873,0.845912,0.852201,0.86478,0.810127,0.867089,0.86478,0.85443,0.93038,1,0.761076,0.974684,0.852201,0.943038,0.822785,1,0.797468,0.867089,0.968553,0.943396,0.990506,0.778481,0.867089,0.962264,1,0.998418,1,0.77057,0.86478,0.86478,0.688679,0.981132,1,0.867089,1,0.78481,0.977848,0.867089,0.726266,1,0.86478,0.845912,0.968354,1,1,0.591772,1,1,1,0.898734,0.968553,0.955696,0.889241]
area_under_roc_curve
0.9241309907650663 [0.851266,0.993711,0.987421,1,0.937107,0.987342,0.96519,0.981013,1,1,0.962264,0.993711,0.993671,1,0.855346,0.987342,1,0.955975,0.962025,0.984177,0.96519,1,0.765823,1,0.981013,0.90566,0.924528,0.857595,0.880503,0.927215,0.686709,1,0.955975,0.930818,0.882911,0.971519,0.927215,0.96519,0.996835,0.996855,0.908228,0.838608,0.993671,0.958861,1,0.882911,1,0.955696,0.937107,0.880503,0.899371,0.995253,0.889241,0.786164,0.958861,0.917722,1,0.871835,0.718354,0.735849,0.851266,1,0.993711,0.882911,0.924051,0.849057,0.962264,1,0.537975,0.863924,0.754717,1,0.856013,1,0.806962,0.767296,0.987421,0.660377,1,1,0.969937,1,0.882911,0.993671,0.838608,0.968354,1,0.987421,0.698113,1,0.893082,1,0.882911,0.962264,0.977987,0.876582,0.936709,0.955975,0.920886,0.924051]
area_under_roc_curve
0.9091228554653293 [0.839623,0.952532,1,1,0.848101,0.880503,0.848101,0.987421,0.943038,1,1,1,0.870253,1,1,0.987421,0.993711,0.880503,0.954114,0.918239,0.966772,0.993671,0.641509,0.993671,0.928797,0.91195,0.415094,0.810127,0.886076,1,1,1,0.974843,0.962264,0.748418,1,0.982595,0.941456,0.993671,0.971698,0.77044,0.871835,1,0.892405,0.993671,0.892405,1,0.946203,0.971698,0.726415,0.808544,0.936709,0.97943,0.803797,0.772152,0.946541,1,0.764151,1,0.892405,0.821203,0.993671,1,0.892405,0.993711,0.971698,0.623418,0.993711,0.626582,0.666667,1,0.987421,0.941456,0.767405,0.870253,0.96519,0.993711,0.937107,1,1,0.734177,0.962264,0.465409,1,0.803797,0.778481,1,0.803797,1,0.920886,0.981013,1,0.867089,0.96519,0.996855,0.90981,0.918239,0.996835,0.829114,0.943396]
area_under_roc_curve
0.8992843672876362 [0.320755,0.981013,0.943396,1,0.982595,0.987421,1,0.993711,0.987342,1,1,0.993711,0.998418,0.990506,1,0.849057,0.981132,0.886792,0.882911,0.918239,0.935127,0.881329,0.981132,1,0.920886,0.90566,0.899371,0.677215,0.873418,0.86478,1,0.993671,0.676101,0.893082,0.742089,0.952532,0.914557,0.89557,0.993671,0.993711,0.789308,0.746835,0.873418,0.873418,1,0.85443,1,0.977848,1,0.871069,0.873418,0.993671,0.984177,0.873418,0.928797,0.90566,1,0.525157,0.996835,0.867089,0.981013,0.64557,0.756329,0.816456,1,0.874214,0.920886,1,0.808544,0.86478,0.676101,1,0.952532,0.65981,0.780063,0.813291,0.814465,0.676101,1,0.987342,0.873418,0.981132,1,0.968553,0.810127,0.859177,1,0.816456,0.962264,0.943038,0.914557,0.993671,0.884494,0.984177,0.58805,1,0.606918,0.946203,0.803797,0.899371]
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
0.26133449078641047
kappa
0.2552268244575937
kappa
0.25584328564108627
kappa
0.2052666850632712
kappa
0.25519744368614145
kappa
0.22348484848484848
kappa
0.2495072920772566
kappa
0.281437125748503
kappa
0.1864086089321613
kappa
0.17438058849017213
kb_relative_information_score
58.23381776838064
kb_relative_information_score
55.309642603039265
kb_relative_information_score
57.72452364404185
kb_relative_information_score
52.78172824556493
kb_relative_information_score
53.8728308155574
kb_relative_information_score
50.569048283658475
kb_relative_information_score
51.75248810673443
kb_relative_information_score
53.03442752650575
kb_relative_information_score
53.7401533916554
kb_relative_information_score
52.161842744444485
mean_absolute_error
0.01770836777471978
mean_absolute_error
0.018290825391052305
mean_absolute_error
0.01803217662642821
mean_absolute_error
0.018276445606436882
mean_absolute_error
0.018319023006855423
mean_absolute_error
0.01823990668448327
mean_absolute_error
0.018340188047096008
mean_absolute_error
0.018279561971220076
mean_absolute_error
0.018179816308079567
mean_absolute_error
0.018210494764762823
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
0.26875
predictive_accuracy
0.2625
predictive_accuracy
0.2625
predictive_accuracy
0.2125
predictive_accuracy
0.2625
predictive_accuracy
0.23125
predictive_accuracy
0.25625
predictive_accuracy
0.2875
predictive_accuracy
0.19375
predictive_accuracy
0.18125
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
0.26875 [0,0,1,1,0,0,0,1,1,1,0,0.5,0.5,0,1,0,0.5,0,0,0,0,0,0,0,1,0,0,0.5,0,0,0.5,0.5,0,0,0,0,0,0,1,0.5,0,0,1,0,1,0,0,0,0,0,0.5,0.5,0,0,0,0,1,0,1,0,0,0,0.5,0,1,0,0.5,1,1,0,0,0,0,0,0,0.5,0.5,0,1,0.5,0,0.5,0,0,0,0,0,0,0.5,0,0,1,0,0,0,0.5,0,0.5,0,1]
recall
0.2625 [0,0,0.5,1,1,1,0.5,1,0.5,1,0,0.5,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0.5,0,0,0,0,0,0.5,0,0,0.5,0,0.5,0,0,0,0,0,0.5,0,1,0,0,0,0.5,1,1,0,0,1,0,0,1,1,0,0.5,1,0,0,0,1,1,0.5,0.5,0,0,0,0,1,0,0.5,0,0,1,0,0,0,0,0,0.5,0,0]
recall
0.2625 [0,0.5,0,0,0,0.5,0,0,0,1,0.5,0.5,0,1,0,1,0,0,0,0,0,0,0.5,1,1,0,0,0,0,1,1,1,0.5,0,1,0,0,0,0,0,0,0,1,0,0.5,1,0,0,0.5,0,0,0,1,0,1,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0.5,0,0,0,0,0,0.5,1,0,0,0.5,0,1,1,0,0.5,0,0,0,0,0.5,0,1,0.5,0,1,0]
recall
0.2125 [0,0,1,1,0,0,1,0.5,0.5,1,1,0,0,1,1,0.5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0.5,0,1,0,0,0,0.5,0,0,0,0,0,0,0,0,0,1,0.5,1,0,0,0,0.5,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,1,0.5,1,0,0,0.5,0,0,1,0,0,0,0,0,0,0,0.5,0,0,1,0,0]
recall
0.2625 [0,0,1,0,1,0.5,0,0.5,1,1,0.5,0.5,0,0,1,0,0,0,1,0,0,0.5,0.5,0.5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.5,0,1,0,0,0,0,0.5,1,0.5,0,0,0,0,0,0,0,1,0.5,1,0,0,0,0,0,0.5,0,0,0.5,0,0,0,0.5,0,0,0,1,0,0.5,1,1,0,1,0,0.5,1,0,1,0,0,0,0,0.5,0,0,0.5,0,0,0,0,0]
recall
0.23125 [0,1,0.5,1,0,0,0,0.5,0,0.5,0.5,0.5,0,0,0,0.5,0.5,0,0,0,0,0,0,0.5,0.5,0,0,0,0,0,1,1,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,1,0.5,0,0,0,0,0,0,0,0,1,0,1,0,0,0.5,1,0,0,1,0,0.5,0,0,0,0,0,1,0.5,0,0,0,1,1,0,0.5,0,0.5,0,0,1,1,0,0.5,0,0,0,0,0,1,0,0,0,0]
recall
0.25625 [0,1,0,0.5,1,0.5,0,0.5,0,1,1,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0.5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0.5,0,0,1,1,1,1,0,0,0,0,0,1,0,1,0,0.5,0,0.5,1,0,0,0,1,1,0.5,1,1,0,0,0,0.5,0.5]
recall
0.2875 [0,0,1,0.5,0,0.5,0,0.5,1,0,0,1,0.5,1,0,0,1,0,0,0,0,1,0,1,0.5,0,1,0,1,0.5,0,1,0,0,0,0,0,0,0.5,1,0,0,1,0,1,0,0,0,0,0,0,0.5,0.5,0,0,0,1,0,0.5,0,0,1,0,0,0.5,0,0,0.5,0,0,0,0,0,1,0,0,1,0,1,1,0,1,0,0.5,0,0.5,1,0,0,1,0,0.5,0,0,1,0,0.5,0,0,0]
recall
0.19375 [0,0,1,1,0,0,0.5,0,0,0,0,1,0,0.5,0.5,0,1,0,0,0,0,0,0,0.5,0.5,0,0,0,0,1,0.5,1,1,0,0,0,0,0,0,0,0,0,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0.5,0,0,0,1,0.5,0,0,0,1,0,0,0.5,0,0,0,0,0,0,0.5,0,0,0,0.5,0,0]
recall
0.18125 [0,0,0,0,0.5,0,0.5,1,0,0,1,1,0,0,0.5,0,0,0,0,0,0,0.5,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0.5,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0.5,0.5,0,0,0,0,1,0,0,0,0,0,0,0.5,0,0,1,0,0,0,0,0,0,1,0,0.5,0,0]
relative_absolute_error
0.8943620088242298
relative_absolute_error
0.9237790601541553
relative_absolute_error
0.9107159912337464
relative_absolute_error
0.9230528084059015
relative_absolute_error
0.9252031821644137
relative_absolute_error
0.9212074083072344
relative_absolute_error
0.926272123590706
relative_absolute_error
0.9232102005666689
relative_absolute_error
0.9181725408120979
relative_absolute_error
0.9197219578163026
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
0.09261590097307276
root_mean_squared_error
0.09497905707303425
root_mean_squared_error
0.0936441065721702
root_mean_squared_error
0.09517462692472481
root_mean_squared_error
0.09510588530274859
root_mean_squared_error
0.0946987104848579
root_mean_squared_error
0.09468548155194302
root_mean_squared_error
0.09416405081713543
root_mean_squared_error
0.09503288507217666
root_mean_squared_error
0.09493010653171652
root_relative_squared_error
0.9308248277224053
root_relative_squared_error
0.954575440160623
root_relative_squared_error
0.9411586828119469
root_relative_squared_error
0.9565409911253595
root_relative_squared_error
0.9558501118296759
root_relative_squared_error
0.9517578509356617
root_relative_squared_error
0.9516248951573119
root_relative_squared_error
0.9463843190921116
root_relative_squared_error
0.9551164319072016
root_relative_squared_error
0.9540834687096057
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
1904.1111550000003
usercpu_time_millis
1706.0130739999995
usercpu_time_millis
1706.8652299999999
usercpu_time_millis
1725.3011069999982
usercpu_time_millis
1705.3888449999997
usercpu_time_millis
1702.4497220000007
usercpu_time_millis
1702.6370010000012
usercpu_time_millis
1709.3388799999952
usercpu_time_millis
1702.7670970000058
usercpu_time_millis
1739.8269990000017
usercpu_time_millis_testing
101.42379000000012
usercpu_time_millis_testing
100.66775700000008
usercpu_time_millis_testing
101.60057199999972
usercpu_time_millis_testing
101.0327379999989
usercpu_time_millis_testing
100.00064499999972
usercpu_time_millis_testing
100.65511600000043
usercpu_time_millis_testing
100.60593800000106
usercpu_time_millis_testing
108.13023899999763
usercpu_time_millis_testing
100.51085800000337
usercpu_time_millis_testing
107.05744499999881
usercpu_time_millis_training
1802.6873650000002
usercpu_time_millis_training
1605.3453169999993
usercpu_time_millis_training
1605.2646580000003
usercpu_time_millis_training
1624.2683689999992
usercpu_time_millis_training
1605.3882
usercpu_time_millis_training
1601.7946060000004
usercpu_time_millis_training
1602.0310630000001
usercpu_time_millis_training
1601.2086409999977
usercpu_time_millis_training
1602.2562390000026
usercpu_time_millis_training
1632.769554000003