6024679
1
Jan van Rijn
9954
Supervised Classification
6969
sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier)(1)
4059364
bootstrap
false
6902
class_weight
null
6902
criterion
"entropy"
6902
max_depth
null
6902
max_features
0.4312688857604513
6902
max_leaf_nodes
null
6902
min_impurity_split
1e-07
6902
min_samples_leaf
4
6902
min_samples_split
20
6902
min_weight_fraction_leaf
0.0
6902
n_estimators
100
6902
n_jobs
1
6902
oob_score
false
6902
random_state
16479
6902
verbose
0
6902
warm_start
false
6902
axis
0
6947
categorical_features
[]
6947
copy
true
6947
fill_empty
0
6947
missing_values
"NaN"
6947
strategy
"most_frequent"
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
true
6948
threshold
0.0
6949
openml-pimp
openml-python
Sklearn_0.18.1.
study_71
1491
one-hundred-plants-margin
https://www.openml.org/data/download/1592283/phpCsX3fx
-1
12972473
description
https://api.openml.org/data/download/12972473/description.xml
-1
12972474
predictions
https://api.openml.org/data/download/12972474/predictions.arff
area_under_roc_curve
0.9944203756313128 [0.981613,0.999527,0.999724,1,0.998935,0.993963,0.999329,0.998856,0.996567,0.999645,0.997199,1,0.997554,0.986979,0.999763,0.998619,1,0.983152,0.985756,0.969618,0.998895,0.999605,0.993608,1,0.99779,0.965988,0.987847,0.996962,0.99925,0.999724,0.999921,1,0.997554,0.986229,0.997988,0.998856,0.987295,0.990649,0.998974,0.999842,0.997199,0.999014,0.999882,0.998974,0.998737,0.999724,0.999053,0.997751,0.996409,0.99491,0.996094,0.993647,0.997869,0.976247,0.995028,0.973801,1,0.999842,0.995699,0.984612,1,0.998501,0.994239,0.997988,0.998027,0.994634,0.979088,1,0.991201,0.986821,0.994121,0.998422,0.993371,0.999014,0.964568,0.996212,0.999369,0.980903,1,0.999961,0.995739,0.995384,0.996646,0.994594,0.993726,0.995344,0.999921,0.990649,0.998146,1,0.999882,0.999684,0.99779,0.989662,0.999053,0.99929,0.99712,0.993687,0.962437,0.99858]
average_cost
0
f_measure
0.7401505900142734 [0.592593,0.842105,0.941176,1,0.9375,0.645161,0.882353,0.857143,0.714286,0.967742,0.6875,0.882353,0.685714,0.48,0.9375,0.875,0.888889,0.444444,0.529412,0.16,0.769231,0.969697,0.705882,1,0.8,0.380952,0.3125,0.756757,0.882353,0.914286,0.9375,1,0.875,0.5625,0.810811,0.736842,0.387097,0.52381,0.909091,0.941176,0.727273,0.848485,0.909091,0.882353,0.848485,0.827586,0.83871,0.8,0.8125,0.83871,0.592593,0.6,0.742857,0.48,0.551724,0.105263,1,0.888889,0.5625,0.642857,0.941176,0.714286,0.709677,0.810811,0.777778,0.764706,0.387097,0.864865,0.434783,0.434783,0.6,0.8,0.648649,0.882353,0.8,0.896552,0.888889,0.521739,0.9375,0.967742,0.709677,0.666667,0.774194,0.580645,0.727273,0.875,0.9375,0.555556,0.774194,1,0.864865,0.780488,0.866667,0.315789,0.787879,0.787879,0.727273,0.645161,0.454545,0.774194]
kappa
0.7518939393939394
kb_relative_information_score
1134.3565131621458
mean_absolute_error
0.013588146177212686
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]
precision
0.7567530773828254 [0.727273,0.727273,0.888889,1,0.9375,0.666667,0.833333,0.789474,0.833333,1,0.6875,0.833333,0.631579,0.666667,0.9375,0.875,0.8,0.545455,0.5,0.222222,0.652174,0.941176,0.666667,1,0.666667,0.8,0.3125,0.666667,0.833333,0.842105,0.9375,1,0.875,0.5625,0.714286,0.636364,0.4,0.423077,0.882353,0.888889,0.705882,0.823529,0.882353,0.833333,0.823529,0.923077,0.866667,0.857143,0.8125,0.866667,0.727273,0.642857,0.684211,0.666667,0.615385,0.333333,1,0.8,0.5625,0.75,0.888889,0.576923,0.733333,0.714286,0.7,0.722222,0.4,0.761905,0.714286,0.714286,0.642857,0.736842,0.571429,0.833333,0.857143,1,0.8,0.857143,0.9375,1,0.733333,0.714286,0.8,0.6,0.705882,0.875,0.9375,0.5,0.8,1,0.761905,0.64,0.928571,1,0.764706,0.764706,0.705882,0.666667,0.833333,0.8]
predictive_accuracy
0.754375
prior_entropy
6.6438561897747395
recall
0.754375 [0.5,1,1,1,0.9375,0.625,0.9375,0.9375,0.625,0.9375,0.6875,0.9375,0.75,0.375,0.9375,0.875,1,0.375,0.5625,0.125,0.9375,1,0.75,1,1,0.25,0.3125,0.875,0.9375,1,0.9375,1,0.875,0.5625,0.9375,0.875,0.375,0.6875,0.9375,1,0.75,0.875,0.9375,0.9375,0.875,0.75,0.8125,0.75,0.8125,0.8125,0.5,0.5625,0.8125,0.375,0.5,0.0625,1,1,0.5625,0.5625,1,0.9375,0.6875,0.9375,0.875,0.8125,0.375,1,0.3125,0.3125,0.5625,0.875,0.75,0.9375,0.75,0.8125,1,0.375,0.9375,0.9375,0.6875,0.625,0.75,0.5625,0.75,0.875,0.9375,0.625,0.75,1,1,1,0.8125,0.1875,0.8125,0.8125,0.75,0.625,0.3125,0.75]
relative_absolute_error
0.6862700089501343
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.07413262827572041
root_relative_squared_error
0.7450609476165326
total_cost
0
area_under_roc_curve
0.9936748666507442 [1,1,1,1,0.996835,1,1,1,0.993671,1,0.990506,1,1,0.971519,1,1,1,0.974684,0.977848,0.974843,1,1,0.993711,1,1,0.987342,0.984177,1,0.996835,1,0.996835,1,0.996835,0.990506,1,0.993711,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.981013,0.987342,1,0.993711,0.993671,1,1,1,1,0.993711,0.993671,1,1,1,1,1,1,0.993671,1,1,0.955975,0.993671,1,0.984177,1,1,1,1,0.981013,1,1,1,0.996835,1,1,0.996835,0.958861,1,0.993671,0.993671,1,1,1,0.996835,0.984177,1,1,1,0.993671,0.863924,1]
area_under_roc_curve
0.9944296831462461 [1,1,1,1,1,0.993711,0.996835,1,1,1,0.996835,1,0.990506,0.968354,1,1,1,0.96519,0.996835,0.987421,1,0.993711,1,1,1,0.936709,0.987342,0.993671,1,1,1,1,1,0.996835,1,1,0.974843,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,0.971519,1,1,0.993711,0.943396,1,1,1,0.933544,1,1,1,0.990506,1,1,1,1,0.974843,0.993711,1,1,1,1,1,1,1,0.987342,1,0.996835,0.996835,0.993671,0.974843,0.987421,0.996835,1,1,0.993671,0.993671,1,1,1,1,0.993671,0.996835,1,1,0.977848,1,1]
area_under_roc_curve
0.9943881358968236 [0.993671,1,1,1,0.993671,0.990506,1,1,0.993671,1,1,1,1,0.996835,1,1,1,0.990506,0.987421,0.908228,1,1,0.968354,1,0.987421,0.974684,1,1,1,1,1,1,1,0.962025,0.993711,1,0.993711,0.974843,1,1,1,1,1,1,0.996835,1,0.996835,1,0.984177,1,0.993671,1,1,0.990506,1,0.993671,1,1,0.987421,0.981013,1,0.993711,1,1,1,0.984177,0.990506,1,0.981132,0.987342,0.996835,1,0.993711,0.996835,1,1,1,0.990506,1,1,1,0.996835,1,1,1,1,1,1,0.993671,1,1,1,0.993711,0.981013,1,1,0.996835,0.993671,0.981132,1]
area_under_roc_curve
0.9935972454422417 [0.996835,0.993671,1,1,1,0.984177,1,1,0.990506,1,1,1,1,1,1,1,1,0.987342,0.918239,0.987342,1,1,1,1,0.993711,0.96519,0.977848,1,1,1,1,1,1,0.981013,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.939873,1,0.990506,1,1,1,0.987342,1,1,1,1,0.996835,0.996835,0.952532,1,1,0.958861,0.987342,0.993671,0.981132,1,1,0.984177,1,0.939873,1,1,0.987421,1,1,0.987342,1,1,1,1,1,1,1,1,0.987421,1,1,1,0.987342,0.996835,1,0.993671]
area_under_roc_curve
0.992415512299976 [0.987342,1,1,1,1,0.996835,1,1,1,0.996835,0.996835,1,1,0.943396,1,1,1,0.996835,1,0.990506,0.984177,1,1,1,1,0.96519,0.984177,1,1,1,1,1,0.996835,0.974684,1,1,1,0.987342,1,1,1,1,1,1,1,0.993711,1,1,1,1,0.993711,0.981132,0.996835,0.968553,0.971519,0.952532,1,0.996835,0.993671,0.993711,1,1,1,1,0.996835,1,0.918239,1,0.971519,0.984177,1,1,1,1,0.863924,1,0.996835,0.968354,1,1,1,1,0.993671,0.993671,0.981132,1,1,1,1,1,1,0.996835,1,0.987421,0.996835,1,0.996835,1,0.974843,0.996835]
area_under_roc_curve
0.9928819859087652 [0.917722,1,1,1,1,0.987342,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,0.933544,1,1,0.971519,1,0.993671,0.990506,0.977848,1,1,1,1,1,0.977848,0.993671,0.993711,1,0.949367,1,1,1,1,0.990506,1,1,1,1,1,1,1,0.977848,1,1,1,1,0.987342,0.958861,1,1,0.993671,1,1,0.993671,0.987421,1,1,1,1,1,0.996835,1,0.984177,0.993671,1,1,0.927215,1,1,0.987342,1,1,1,0.990506,1,0.996835,1,1,1,0.987421,1,1,1,1,1,1,1,1,1,0.981132,0.993711,0.990506]
area_under_roc_curve
0.9954633886633231 [0.996835,1,1,1,1,1,1,1,0.987421,1,1,1,0.987342,1,1,1,1,0.993711,0.993671,0.977848,0.996835,1,1,1,0.993671,0.886792,0.987421,0.981013,1,1,1,1,1,0.993711,1,1,0.993671,1,1,1,1,0.996835,1,1,1,1,1,0.987342,1,1,1,1,1,1,0.996835,0.987342,1,1,0.996835,0.993711,1,1,1,0.984177,1,0.987421,0.968553,1,0.996835,1,0.993711,1,1,1,0.996835,1,1,0.993711,1,1,0.990506,1,0.996835,1,1,1,1,0.924528,1,1,1,1,0.993671,0.981132,0.993711,1,1,1,0.949367,1]
area_under_roc_curve
0.9963281685375366 [1,1,1,1,1,1,1,1,0.993711,1,1,1,0.996835,1,1,0.996835,1,0.993711,0.987342,0.987342,1,1,1,1,1,0.974843,0.981132,0.996835,1,0.996835,1,1,1,1,1,1,0.984177,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.996835,0.987342,1,1,0.977848,1,1,0.993671,1,1,0.993671,0.993711,1,1,0.955975,0.981132,1,0.990506,0.987342,1,1,1,1,0.993671,1,1,0.993711,1,1,0.996835,0.993711,0.993671,0.996835,0.971519,0.996835,1,0.987421,1,1,1,1,1,0.981132,1,1,0.993671,1,0.981013,1]
area_under_roc_curve
0.9967234893718651 [0.962264,1,1,1,1,1,1,1,0.996835,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.987421,1,1,1,0.987421,1,1,1,1,1,1,1,1,1,0.996835,0.968354,1,1,0.981132,1,1,0.990506,1,1,1,1,0.993711,1,1,0.996835,1,0.946203,1,0.955975,1,1,1,0.993671,1,1,0.996835,1,1,1,1,1,0.993671,0.993711,1,0.993711,0.987342,1,1,0.987342,1,1,1,1,1,1,1,0.987421,0.996835,1,1,0.987342,1,1,1,1,1,0.996835,1,1,0.993711,1,0.987342,1]
area_under_roc_curve
0.9970033735371386 [0.930818,1,1,1,1,1,1,0.993711,1,1,1,1,1,0.987342,1,1,1,0.987421,0.993671,1,1,1,1,1,1,0.930818,1,1,1,1,1,1,1,0.993711,1,1,0.990506,1,0.993671,1,0.993711,1,1,1,1,1,1,0.996835,1,1,1,0.993671,1,0.981013,1,0.993711,1,1,0.996835,0.996835,1,1,0.977848,1,0.987421,1,0.996835,1,1,0.993711,0.993711,1,0.993671,1,0.993671,0.996835,1,1,1,1,1,0.974843,1,0.981132,1,1,0.996835,0.996835,1,1,1,1,1,1,1,1,1,1,1,1]
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
0.7473351756810107
kappa
0.7536809694864406
kappa
0.7158306034652879
kappa
0.7157745144481289
kappa
0.7662481244570797
kappa
0.7472952696833294
kappa
0.7536031589338598
kappa
0.7599305061991629
kappa
0.7915103652517275
kappa
0.7662481244570797
kb_relative_information_score
113.39363968109457
kb_relative_information_score
112.64282707297185
kb_relative_information_score
112.59897644382758
kb_relative_information_score
112.7631424970498
kb_relative_information_score
112.34036621016989
kb_relative_information_score
111.94944197537745
kb_relative_information_score
114.7879020339534
kb_relative_information_score
114.28724419100469
kb_relative_information_score
114.87149350917576
kb_relative_information_score
114.72147954751964
mean_absolute_error
0.013533292325348792
mean_absolute_error
0.01366636301772775
mean_absolute_error
0.013652366431664082
mean_absolute_error
0.013652342635601517
mean_absolute_error
0.013591415492977238
mean_absolute_error
0.013750927008057304
mean_absolute_error
0.0134002484079663
mean_absolute_error
0.013481452517893216
mean_absolute_error
0.013543973849147895
mean_absolute_error
0.01360908008574252
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
0.75
predictive_accuracy
0.75625
predictive_accuracy
0.71875
predictive_accuracy
0.71875
predictive_accuracy
0.76875
predictive_accuracy
0.75
predictive_accuracy
0.75625
predictive_accuracy
0.7625
predictive_accuracy
0.79375
predictive_accuracy
0.76875
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
0.75 [1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,0,1,1,1,0,0.5,0,1,1,1,1,1,0.5,0,1,0.5,1,1,1,0.5,0,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,0,1,0,1,1,0,1,1,1,1,1,1,1,0.5,1,0,0,0.5,1,0.5,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,1,1,0,1,1,1,1,0.5,0.5,0.5,1,1,0,1]
recall
0.75625 [1,1,1,1,1,1,0.5,1,0.5,1,1,1,0.5,0.5,1,1,1,0,0.5,0,1,1,1,1,1,0.5,0.5,0.5,1,1,1,1,1,0.5,1,1,0,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,0,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,0,1,0,0,1,1,1,1,1,1,1,0.5,1,0.5,0.5,0.5,0,0,1,1,0.5,0.5,0.5,1,1,1,1,0,0.5,1,1,0,0.5,1]
recall
0.71875 [0.5,1,1,1,0.5,0.5,1,1,0,1,1,1,1,0.5,1,0.5,1,0.5,0,0,1,1,0.5,1,1,0,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,1,0,1,1,1,0,1,1,0.5,1,1,0.5,0.5,1,0,0,0.5,1,0,1,1,1,1,0.5,0.5,1,1,0.5,1,0.5,1,1,1,0.5,1,1,1,1,1,0,1,1,0.5,0.5,0,0.5]
recall
0.71875 [0.5,1,1,1,1,0,1,1,0.5,1,0,1,1,0.5,1,1,1,0.5,0,0,1,1,0.5,1,1,0,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0,1,0.5,1,1,0,0.5,1,1,1,1,0.5,1,0,1,0,0.5,0,0.5,0,1,1,0.5,1,0,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0,0,1,0,0.5,1,0,0.5]
recall
0.76875 [0.5,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0,1,1,1,0.5,1,0,0.5,1,1,1,1,0.5,0,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,0,1,0.5,1,0.5,0,0,0.5,0,0,0,1,1,1,1,1,1,1,1,0.5,1,0,1,0.5,0.5,1,1,1,1,0.5,1,1,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0,0.5]
recall
0.75 [0.5,1,1,1,1,0.5,1,1,1,0.5,0,1,1,1,1,1,1,0.5,1,0,1,1,0.5,1,1,0.5,0.5,1,1,1,1,1,0.5,1,0,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,0,1,0.5,1,1,1,1,0,0,1,1,0,1,1,0.5,0,1,1,0.5,1,1,0.5,0.5,0.5,1,1,1,0.5,1,1,0,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0,1,1,1,1,0,0,0.5]
recall
0.75625 [0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,0,1,1,1,0,1,0.5,1,1,0.5,1,1,0,0,0.5,1,1,0.5,1,1,1,1,1,0,1,1,1,0.5,0.5,1,1,1,0.5,0.5,0.5,1,1,0,1,1,0,0.5,0,1,1,0.5,0,1,1,1,0.5,1,1,0,1,0.5,0,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,1,1,1,1,0,0,1,1,1,0.5,0,1,0.5,1,0,0.5,1]
recall
0.7625 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1,1,0.5,0,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5,0.5,1,0.5,0,1,1,0.5,1,1,1,1,1,1,0,1,1,0,1,0,1,1,0,1,1,1,1,1,1,0,0,0,1,0.5,0.5,1,0,1,1,1,1,1,0,1,1,0.5,1,0,1]
recall
0.79375 [0,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,1,0,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,1,0.5,1,1,1,0,1,0.5,0.5,1,0.5,0.5,0,1,1,0.5,0,1,1,0.5,1,1,1,0.5,1,0.5,0,1,0,0.5,1,1,0.5,1,1,1,1,1,1,1,0,0.5,1,1,0.5,1,1,1,1,1,0,1,1,0,1,0.5,1]
recall
0.76875 [0,1,1,1,1,1,1,0,1,1,1,1,0.5,0.5,0.5,1,1,0,0,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1,1,0,1,0.5,1,1,1,0.5,0.5,1,0.5,1,1,1,1,0.5,0.5,0.5,0,0.5,0,1,1,1,1,1,1,0,1,1,1,0.5,1,0.5,0,0,1,1,1,0,0.5,1,1,1,1,1,0,1,0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0.5,1,1]
relative_absolute_error
0.6834996123913519
relative_absolute_error
0.6902203544306933
relative_absolute_error
0.6895134561446495
relative_absolute_error
0.6895122543233079
relative_absolute_error
0.6864351259079402
relative_absolute_error
0.694491263033196
relative_absolute_error
0.6767802226245595
relative_absolute_error
0.680881440297636
relative_absolute_error
0.6840390832902966
relative_absolute_error
0.6873272770577019
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
0.0738376266657378
root_mean_squared_error
0.07450882447820445
root_mean_squared_error
0.07457693892459373
root_mean_squared_error
0.07436195449880906
root_mean_squared_error
0.07431359427328245
root_mean_squared_error
0.07513028985886674
root_mean_squared_error
0.07336603051204953
root_mean_squared_error
0.07369856007752328
root_mean_squared_error
0.07352629812348468
root_mean_squared_error
0.0739881488025819
root_relative_squared_error
0.7420960698805841
root_relative_squared_error
0.7488418617110665
root_relative_squared_error
0.7495264376549333
root_relative_squared_error
0.7473657628788795
root_relative_squared_error
0.7468797243247924
root_relative_squared_error
0.7550878237954673
root_relative_squared_error
0.737356350200709
root_relative_squared_error
0.7406983980806373
root_relative_squared_error
0.7389671003012451
root_relative_squared_error
0.7436088742762036
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
6380.089317
usercpu_time_millis
6249.009655000009
usercpu_time_millis
6248.352689999991
usercpu_time_millis
6256.9468709999965
usercpu_time_millis
6210.612087999976
usercpu_time_millis
6572.055060000025
usercpu_time_millis
6521.172407999984
usercpu_time_millis
6255.49873899999
usercpu_time_millis
6261.098086999994
usercpu_time_millis
6259.348325000019
usercpu_time_millis_testing
44.01249899999016
usercpu_time_millis_testing
33.91370200000665
usercpu_time_millis_testing
34.481673000016144
usercpu_time_millis_testing
33.64409499999965
usercpu_time_millis_testing
33.43411399998786
usercpu_time_millis_testing
37.30346900002246
usercpu_time_millis_testing
34.09503699998595
usercpu_time_millis_testing
34.65043399998535
usercpu_time_millis_testing
37.29082499998526
usercpu_time_millis_testing
33.65642599999319
usercpu_time_millis_training
6336.07681800001
usercpu_time_millis_training
6215.095953000002
usercpu_time_millis_training
6213.871016999974
usercpu_time_millis_training
6223.302775999997
usercpu_time_millis_training
6177.177973999988
usercpu_time_millis_training
6534.751591000003
usercpu_time_millis_training
6487.077370999998
usercpu_time_millis_training
6220.848305000004
usercpu_time_millis_training
6223.807262000008
usercpu_time_millis_training
6225.691899000026