8806923
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)
6782004
axis
0
7660
categorical_features
[]
7660
copy
true
7660
fill_empty
0
7660
missing_values
"NaN"
7660
strategy
"most_frequent"
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
2562.1886440879002
7708
cache_size
200
7708
class_weight
null
7708
coef0
-0.44508343195357725
7708
decision_function_shape
null
7708
degree
3
7708
gamma
0.0050412128053365195
7708
kernel
"poly"
7708
max_iter
-1
7708
probability
true
7708
random_state
2095
7708
shrinking
false
7708
tol
0.00034599948612205764
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
18529819
description
https://api.openml.org/data/download/18529819/description.xml
-1
18529820
predictions
https://api.openml.org/data/download/18529820/predictions.arff
area_under_roc_curve
0.9855689709595958 [0.959083,0.998343,0.998816,0.99929,0.997711,0.989149,0.992582,0.996488,0.993647,0.999882,0.996765,0.997593,0.992306,0.922901,0.997475,0.989426,0.998619,0.97455,0.948903,0.948074,0.982402,0.992977,0.962358,0.998501,0.992188,0.974392,0.949416,0.99203,0.997396,0.999921,0.983586,0.999882,0.992227,0.983546,0.975813,0.998185,0.97017,0.965949,0.998303,0.991201,0.987255,0.978338,0.993647,0.997277,0.982797,0.996449,0.998619,0.995699,0.994949,0.991122,0.985598,0.99057,0.995107,0.973682,0.97893,0.975497,1,0.996409,0.994121,0.975458,0.995147,0.966856,0.993726,0.957702,0.998777,0.963936,0.966027,0.995778,0.988005,0.984138,0.974629,0.997988,0.980232,0.997475,0.97455,0.988636,0.998501,0.988834,0.999605,0.998974,0.97459,0.993608,0.979048,0.990807,0.995068,0.993726,0.9942,0.976207,0.998027,0.994673,0.987098,0.999605,0.993371,0.990846,0.969184,0.993766,0.978969,0.996567,0.902462,0.971985]
average_cost
0
f_measure
0.6194816634709341 [0.16,0.827586,0.903226,0.814815,0.866667,0.689655,0.903226,0.666667,0.592593,0.857143,0.8125,0.827586,0.777778,0.416667,0.814815,0.612245,0.903226,0.242424,0.294118,0.093023,0.533333,0.64,0.318182,0.857143,0.4,0.272727,0.25,0.789474,0.64,0.666667,0.83871,0.967742,0.5,0.375,0.385965,0.518519,0.311111,0.222222,0.848485,0.72,0.580645,0.777778,0.705882,0.787879,0.814815,0.32,0.814815,0.75,0.866667,0.648649,0.434783,0.764706,0.740741,0.434783,0.5,0.173913,0.896552,0.8125,0.631579,0.689655,0.72,0.615385,0.551724,0.419355,0.848485,0.307692,0.285714,0.857143,0.580645,0.555556,0.285714,0.692308,0.4,0.774194,0.6875,0.764706,0.774194,0.296296,0.969697,0.9375,0.2,0.8,0.533333,0.666667,0.666667,0.785714,0.896552,0.612245,0.56,0.866667,0.434783,0.827586,0.666667,0.615385,0.648649,0.777778,0.541667,0.758621,0,0.487805]
kappa
0.6098484848484849
kb_relative_information_score
795.0826632946843
mean_absolute_error
0.017123437444443085
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
0.6736890653854464 [0.222222,0.923077,0.933333,1,0.928571,0.769231,0.933333,0.6,0.727273,1,0.8125,0.923077,0.7,0.625,1,0.454545,0.933333,0.235294,0.277778,0.074074,0.571429,0.888889,0.25,0.789474,0.555556,0.5,0.1875,0.681818,0.888889,1,0.866667,1,0.75,0.375,0.268293,0.636364,0.241379,0.272727,0.823529,1,0.6,0.7,0.666667,0.764706,1,0.444444,1,0.75,0.928571,0.571429,0.714286,0.722222,0.909091,0.714286,0.416667,0.285714,1,0.8125,0.545455,0.769231,1,0.8,0.615385,0.282609,0.823529,0.222222,0.6,1,0.6,0.5,0.333333,0.9,0.333333,0.8,0.6875,0.722222,0.8,0.363636,0.941176,0.9375,0.5,0.857143,0.413793,0.6,0.647059,0.916667,1,0.454545,0.777778,0.928571,0.714286,0.923077,0.714286,0.8,0.571429,0.7,0.40625,0.846154,0,0.4]
predictive_accuracy
0.61375
prior_entropy
6.6438561897747395
recall
0.61375 [0.125,0.75,0.875,0.6875,0.8125,0.625,0.875,0.75,0.5,0.75,0.8125,0.75,0.875,0.3125,0.6875,0.9375,0.875,0.25,0.3125,0.125,0.5,0.5,0.4375,0.9375,0.3125,0.1875,0.375,0.9375,0.5,0.5,0.8125,0.9375,0.375,0.375,0.6875,0.4375,0.4375,0.1875,0.875,0.5625,0.5625,0.875,0.75,0.8125,0.6875,0.25,0.6875,0.75,0.8125,0.75,0.3125,0.8125,0.625,0.3125,0.625,0.125,0.8125,0.8125,0.75,0.625,0.5625,0.5,0.5,0.8125,0.875,0.5,0.1875,0.75,0.5625,0.625,0.25,0.5625,0.5,0.75,0.6875,0.8125,0.75,0.25,1,0.9375,0.125,0.75,0.75,0.75,0.6875,0.6875,0.8125,0.9375,0.4375,0.8125,0.3125,0.75,0.625,0.5,0.75,0.875,0.8125,0.6875,0,0.625]
relative_absolute_error
0.8648200729516694
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08850456548158049
root_relative_squared_error
0.8895043513207347
total_cost
0
area_under_roc_curve
0.986449874611894 [0.930818,1,1,1,1,1,1,1,1,1,1,0.990506,1,0.816456,1,0.943396,0.990506,0.987342,0.933544,0.962264,0.962264,1,1,1,1,0.996835,0.946203,0.984177,1,1,1,1,0.993671,0.984177,0.981013,1,0.974843,0.981132,1,1,1,0.993711,1,1,1,0.996835,1,1,1,0.958861,0.996835,1,1,0.993671,0.918239,1,1,1,1,0.971519,1,1,0.996835,0.96519,1,0.96519,0.984177,1,1,0.955975,0.949367,1,0.993671,1,1,0.990506,0.996835,0.971519,1,1,0.993671,1,0.993711,0.987421,1,0.993671,0.993671,0.968354,0.990506,0.993711,0.993671,1,1,0.996835,0.987342,0.996835,0.974843,0.996835,0.89557,0.974843]
area_under_roc_curve
0.9848720742775254 [0.981132,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.81962,1,1,1,0.987342,1,0.930818,0.987421,0.987421,0.949686,1,1,0.977848,0.946203,0.996835,1,1,0.990506,1,0.977848,0.993671,1,0.993711,0.962264,0.930818,1,1,1,0.993711,1,0.996835,0.996835,1,1,1,0.996835,0.993671,1,0.974684,0.987421,0.993671,1,0.981132,1,1,0.981132,0.946203,1,0.981132,1,0.927215,1,0.949367,0.971519,1,1,0.987421,0.987342,1,0.987342,1,1,0.993671,1,0.977848,1,1,0.939873,0.993671,1,0.962264,0.996835,0.993671,1,0.971519,0.996835,1,0.993671,1,0.996835,0.993671,0.958861,0.996835,0.955975,0.993671,0.867089,0.880503]
area_under_roc_curve
0.9854388086139639 [0.984177,1,1,1,0.993671,1,0.90566,0.993671,0.993671,1,1,0.996835,1,0.993671,0.984177,0.993671,1,0.943038,0.968553,0.955696,0.962264,0.981132,0.958861,1,0.987421,0.987342,0.981013,0.993711,1,1,1,1,1,0.977848,0.924528,1,0.974843,0.91195,0.981132,0.987342,0.974684,0.81761,1,1,1,1,1,0.981132,0.987342,1,1,1,1,1,0.993711,0.996835,1,0.996835,0.968553,0.93038,1,0.993711,1,0.842767,0.993671,0.962025,0.933544,1,1,0.984177,0.971519,0.996835,0.987421,1,1,1,0.996835,0.990506,1,0.996835,1,1,0.990506,0.996835,1,1,1,0.974684,1,0.987421,0.955696,1,1,0.990506,0.981013,1,0.981013,1,0.943396,0.974684]
area_under_roc_curve
0.9864550991163122 [0.908228,1,0.990506,0.993711,1,0.981013,0.993711,1,0.990506,1,1,0.996835,0.993711,0.987342,0.993671,0.993671,1,0.993671,0.949686,0.933544,1,1,0.955696,1,0.993711,0.96519,0.93038,1,1,1,1,1,1,0.984177,0.987421,0.993711,0.974843,0.899371,1,1,1,1,0.987421,1,1,1,1,1,1,0.996835,1,1,1,0.958861,0.981132,0.962025,1,1,1,0.990506,1,0.987421,1,0.955975,1,0.987342,0.971519,0.996835,1,0.981013,0.927215,0.996835,0.974843,0.993671,1,0.990506,1,0.996835,1,1,0.987421,1,0.958861,0.990506,0.974843,1,1,1,1,1,0.996835,1,0.968553,1,0.971519,1,0.955696,0.993671,0.81761,0.987342]
area_under_roc_curve
0.9884732505373771 [0.958861,1,1,1,1,1,1,0.996835,0.993711,1,0.987342,1,0.993711,0.792453,0.993711,0.996835,1,0.987342,1,0.993671,0.996835,0.996835,0.96519,1,0.990506,0.977848,0.955696,1,0.993711,1,0.993711,1,0.993671,0.968354,0.987421,1,0.955696,0.971519,1,1,1,1,1,1,1,1,1,1,1,1,1,0.968553,0.987342,1,0.993671,0.990506,1,1,0.993671,0.987421,0.987342,1,0.993711,0.949686,1,1,0.90566,1,0.984177,0.990506,0.974684,1,1,0.981132,0.892405,0.993711,1,0.993671,1,1,0.981132,1,1,0.984177,0.993711,0.943396,1,0.987421,1,1,1,1,1,0.987421,0.936709,0.987421,1,1,0.987421,0.977848]
area_under_roc_curve
0.9861722295199428 [0.908228,1,1,1,1,0.974684,1,0.990506,1,1,1,1,0.943396,0.987421,0.993711,1,1,0.984177,0.937107,0.924051,0.990506,0.962025,0.974684,1,0.990506,0.968354,0.911392,1,1,1,0.993711,1,0.984177,0.993671,0.949686,0.996835,0.955696,0.996835,0.993671,1,0.996835,0.996835,0.993711,1,1,0.993711,1,0.993671,0.990506,0.996835,1,0.987421,1,1,0.968354,0.968354,1,0.996835,1,1,0.987342,0.939873,0.993711,1,1,0.962025,0.993711,1,1,1,0.977848,0.993671,0.974843,0.993711,0.981013,0.987421,1,1,1,1,0.968553,0.996835,0.990506,0.981013,1,1,1,0.981132,1,0.990506,0.981132,1,0.993711,1,0.949367,1,0.974684,1,0.918239,0.977848]
area_under_roc_curve
0.9864506209696676 [0.996835,1,1,1,1,1,1,0.993671,0.993711,1,1,1,0.990506,0.993711,1,0.987342,1,0.968553,0.96519,0.920886,0.993671,1,0.968354,1,0.996835,0.899371,0.937107,0.981013,1,1,0.943038,1,1,0.974843,0.981013,1,0.987342,0.971519,1,1,0.990506,1,1,1,0.955975,1,0.996835,1,1,1,0.993711,0.993671,0.996835,1,0.96519,0.96519,1,0.996835,1,0.993711,1,0.879747,1,0.955696,1,0.918239,0.974843,1,0.974684,1,1,1,0.971519,0.993711,1,0.993711,1,0.955975,1,1,0.952532,1,0.990506,1,1,1,1,0.987421,1,0.996835,1,1,0.987342,0.993711,1,1,0.984177,1,0.882911,0.993671]
area_under_roc_curve
0.986691196958841 [0.977848,1,1,1,1,1,0.996835,1,0.993711,1,0.981132,1,0.987342,1,0.987421,0.981013,1,0.899371,0.917722,0.952532,0.981013,0.996835,0.96519,1,0.987342,0.987421,0.962264,0.987342,0.968553,0.996835,1,1,1,0.987421,0.993671,1,0.971519,0.981013,1,1,0.987342,0.993671,0.990506,1,0.993711,1,1,0.990506,1,0.981132,0.880503,0.993671,1,1,0.996835,0.968354,1,1,0.993671,1,1,0.939873,1,0.987342,1,0.930818,0.918239,0.993671,0.996835,0.952532,1,1,0.993671,1,0.96519,0.987421,1,1,1,1,0.949367,1,0.943038,1,0.984177,0.996835,1,0.987421,1,1,1,1,0.996835,1,1,0.993671,1,1,0.96519,0.96519]
area_under_roc_curve
0.9872402674946261 [0.993711,0.996835,1,1,1,1,0.996835,1,0.984177,1,1,1,1,0.974684,1,1,1,0.962264,0.924051,0.943396,1,0.993671,0.930818,0.990506,0.996835,0.981132,0.981132,1,1,1,1,1,1,0.981132,0.977848,1,0.977848,0.955696,1,1,0.949686,1,0.974684,0.981013,1,0.987342,1,0.993671,0.993711,1,0.958861,0.993671,1,0.889241,0.987342,0.930818,1,0.968553,0.987342,0.981013,0.990506,0.977848,1,0.971519,1,1,0.984177,0.974843,0.993671,0.993711,1,0.993711,0.962025,1,1,0.974684,1,1,1,0.996835,1,0.993711,0.993711,1,1,1,1,0.977848,1,0.996835,1,1,0.990506,0.974684,1,0.984177,1,1,0.917722,1]
area_under_roc_curve
0.9884635478863147 [0.987421,0.996835,1,1,1,1,1,1,0.996835,1,1,1,1,0.905063,1,0.987421,0.981132,1,0.911392,0.974843,0.946203,0.993671,1,1,0.996835,1,0.993711,1,0.990506,1,1,1,1,0.987421,0.971519,1,0.990506,0.996835,1,1,0.993711,0.996835,0.993671,1,0.996835,1,1,1,1,0.993711,0.987342,0.990506,1,0.990506,0.971519,1,1,1,0.993671,0.971519,0.993671,0.993671,0.984177,0.981013,1,0.937107,0.981013,1,0.977848,0.987421,0.987421,1,0.987342,0.996835,0.974684,0.984177,0.993711,1,1,1,1,0.937107,0.974843,1,0.996835,1,1,0.96519,1,1,0.971519,1,1,0.990506,1,1,0.943396,0.996835,0.892405,0.981132]
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
0.5894844872503355
kappa
0.6146709305539105
kappa
0.5769534333070245
kappa
0.6148533996290596
kappa
0.6272310851366293
kappa
0.6084468127096901
kappa
0.6525035539409256
kappa
0.6081839007820523
kappa
0.6021785460573051
kappa
0.6023354899794855
kb_relative_information_score
79.16726761802556
kb_relative_information_score
79.76533795891557
kb_relative_information_score
78.71436609793244
kb_relative_information_score
77.58134575472128
kb_relative_information_score
81.05115068320602
kb_relative_information_score
78.25876064532464
kb_relative_information_score
81.88682772671967
kb_relative_information_score
78.62110423478799
kb_relative_information_score
79.36692576062623
kb_relative_information_score
80.66957681442534
mean_absolute_error
0.017057431617070955
mean_absolute_error
0.01719992548958305
mean_absolute_error
0.01716816498017337
mean_absolute_error
0.017382663925278697
mean_absolute_error
0.017239041049865362
mean_absolute_error
0.017200043815825416
mean_absolute_error
0.01679411664676012
mean_absolute_error
0.01720978570919321
mean_absolute_error
0.017093175400045107
mean_absolute_error
0.016890025810635523
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
0.59375
predictive_accuracy
0.61875
predictive_accuracy
0.58125
predictive_accuracy
0.61875
predictive_accuracy
0.63125
predictive_accuracy
0.6125
predictive_accuracy
0.65625
predictive_accuracy
0.6125
predictive_accuracy
0.60625
predictive_accuracy
0.60625
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
0.59375 [0,0.5,1,0,0.5,1,1,1,0.5,1,0.5,0.5,0.5,0,1,0,0.5,0,0.5,0,0,1,0,1,1,0.5,0,1,0,1,1,1,0,0.5,0,0,1,0,1,0.5,0,1,1,1,1,0,0,1,1,0.5,0.5,1,1,0.5,0,0,1,1,1,0.5,1,0,0.5,1,0,1,0,1,1,0,0,1,1,0.5,1,1,0,0,1,0.5,0,1,1,1,1,0.5,0.5,1,0,1,0.5,1,1,0.5,1,1,1,1,0,1]
recall
0.61875 [0,1,1,0,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,0.5,0.5,0,1,0,0,1,1,0,0,1,1,0,0.5,1,0,1,1,0,0,0,1,0.5,1,1,1,0,0.5,0,1,1,0.5,1,0.5,0.5,1,0.5,1,0,1,1,1,0.5,1,1,0.5,0.5,1,0.5,0,1,0,0,0.5,1,0,0.5,1,0.5,1,0,1,1,0,0.5,1,1,1,0.5,1,1,0,1,0.5,1,0.5,0,0.5,1,1,0.5,0,0]
recall
0.58125 [0,1,1,1,0.5,1,0,0.5,0.5,1,1,0.5,1,0,0,1,1,0,0,0,0,0,0,1,0,0.5,0.5,1,1,0.5,1,1,0.5,0.5,1,1,1,0,0,0.5,0,0,1,1,1,0,0.5,0,0.5,1,1,1,1,0,1,0.5,1,1,1,0.5,0,0,0.5,0,1,0.5,0,1,0,0.5,0.5,0.5,1,1,1,1,0.5,0,1,1,0,1,0.5,0.5,1,0,1,1,0.5,0,0,1,1,0.5,1,1,1,0.5,0,0]
recall
0.61875 [0,1,0.5,0,0.5,0,0,1,0.5,0.5,1,0.5,1,0,1,1,1,0,0,0,1,1,0.5,1,0,0,0.5,1,0.5,1,1,1,0.5,0,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0.5,1,1,0.5,1,0,1,1,1,0.5,1,1,1,0,1,0.5,0,0.5,1,0.5,0,0,1,1,1,1,1,0,1,1,0,0.5,0,0.5,0,1,0,1,1,1,0.5,1,0,1,0.5,1,0.5,0.5,0,0.5]
recall
0.63125 [0,1,1,1,1,0.5,1,0.5,0,0.5,0.5,1,1,0,1,1,1,0.5,1,0.5,1,1,0.5,1,0.5,0,0.5,1,1,0.5,0,1,0,0,1,0,0,0.5,1,0.5,1,1,1,1,1,0,1,1,1,1,0,0,0.5,0,0,0.5,1,0.5,1,1,0.5,1,0,1,0.5,1,0,1,0,0.5,0.5,0.5,1,0,0.5,1,1,0,1,1,0,1,1,0.5,0,0,1,1,1,1,0,0.5,1,0,0.5,1,0.5,1,0,1]
recall
0.6125 [0.5,1,0.5,1,1,0,1,0.5,1,0.5,1,0.5,0,1,0,1,1,0.5,1,0.5,0.5,0,0.5,1,0,0,0,1,1,0,1,1,0.5,0.5,1,0,0,0,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,0,1,0.5,1,0,0,1,1,0.5,1,0,0,1,1,1,0,1,1,1,1,0,0.5,0,1,1,1,1,0.5,1,1,0,0.5,1,0.5,0,1,1,1,0.5,0.5,0,0.5,1,1,1,1,1,1,0,0.5]
recall
0.65625 [0.5,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,0,0,0,0.5,0,1,1,0.5,0,0,1,0,0.5,0.5,0,1,0,0.5,0.5,0.5,0.5,1,0,0.5,0.5,1,1,0,0.5,0,0.5,1,1,0,1,0.5,1,0.5,0,0.5,0.5,0.5,1,0.5,0.5,1,1,1,0,0,0.5,0.5,1,0,1,0.5,1,1,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0.5,1,0,1]
recall
0.6125 [0,0,1,0.5,1,1,1,1,1,0.5,0,1,1,1,0,1,1,0,0,0,0.5,0.5,0.5,1,0,0,1,0.5,0,0.5,1,1,0,0,1,1,0,0,1,0,0,1,0.5,1,0,0,1,0.5,1,0,0,1,0,0,1,0,1,1,1,1,1,0.5,1,1,1,0,0,0.5,0.5,0.5,1,1,1,1,0,1,1,1,1,1,0,1,0.5,1,0,0.5,1,1,0,1,0,1,0.5,1,1,1,1,1,0,0.5]
recall
0.60625 [0,0.5,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,0,0.5,0,0.5,0.5,0,0.5,0.5,0,1,1,0.5,1,1,1,1,0,0.5,0.5,0.5,0,0.5,0,0,1,0,0.5,0.5,0,1,1,1,1,0,0.5,0.5,0,1,0,0.5,0,0.5,0.5,0.5,0.5,0,1,1,1,0.5,0,1,1,0,0,0,1,1,0.5,1,1,1,1,0,1,1,1,1,1,1,1,0,0.5,0.5,0,0.5,0.5,1,0.5,1,1,0,1]
recall
0.60625 [0,0.5,1,0.5,1,1,1,1,0,1,1,1,1,0,0.5,1,0,1,0,0,0,1,1,1,0,1,1,1,0,0,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,0,1,1,0,1,0,0.5,1,0.5,0.5,0.5,0.5,0,1,1,0,0.5,1,0.5,1,0,0,0,0.5,0,0.5,0,1,1,1,1,0,1,1,1,1,0.5,0.5,0,1,0,1,1,0,0,0.5,1,0,0,1]
relative_absolute_error
0.8614864453066124
relative_absolute_error
0.868683105534496
relative_absolute_error
0.8670790394026939
relative_absolute_error
0.8779123194585187
relative_absolute_error
0.8706586388820875
relative_absolute_error
0.8686890816073428
relative_absolute_error
0.8481877094323279
relative_absolute_error
0.869181096423898
relative_absolute_error
0.8632916868709636
relative_absolute_error
0.8530316065977523
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
0.08825579221213975
root_mean_squared_error
0.088499243145569
root_mean_squared_error
0.08861712807527247
root_mean_squared_error
0.08940719445616034
root_mean_squared_error
0.08859524388756741
root_mean_squared_error
0.08899587286705299
root_mean_squared_error
0.0875124359530239
root_mean_squared_error
0.08858006842111268
root_mean_squared_error
0.08864418728727025
root_mean_squared_error
0.08792444979107822
root_relative_squared_error
0.8870040858885986
root_relative_squared_error
0.8894508598311642
root_relative_squared_error
0.8906356479531757
root_relative_squared_error
0.8985761138467487
root_relative_squared_error
0.8904157035911776
root_relative_squared_error
0.8944421763338946
root_relative_squared_error
0.8795330743823879
root_relative_squared_error
0.8902631844146657
root_relative_squared_error
0.8909076032662645
root_relative_squared_error
0.8836739692589348
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
1423.7209489992892
usercpu_time_millis
1329.9730539993107
usercpu_time_millis
1188.3947019996413
usercpu_time_millis
1206.6559709992362
usercpu_time_millis
1216.2496759992791
usercpu_time_millis
1185.3537050010345
usercpu_time_millis
1191.005677999783
usercpu_time_millis
1165.5711960002009
usercpu_time_millis
1197.3344620000717
usercpu_time_millis
1204.0426599996863
usercpu_time_millis_testing
145.82307799992122
usercpu_time_millis_testing
137.37568899978214
usercpu_time_millis_testing
140.01831499990658
usercpu_time_millis_testing
139.82967699939763
usercpu_time_millis_testing
137.55166600003577
usercpu_time_millis_testing
133.09378000030847
usercpu_time_millis_testing
140.89060199967207
usercpu_time_millis_testing
132.72616199992626
usercpu_time_millis_testing
151.17171499969118
usercpu_time_millis_testing
134.66974499988282
usercpu_time_millis_training
1277.897870999368
usercpu_time_millis_training
1192.5973649995285
usercpu_time_millis_training
1048.3763869997347
usercpu_time_millis_training
1066.8262939998385
usercpu_time_millis_training
1078.6980099992434
usercpu_time_millis_training
1052.259925000726
usercpu_time_millis_training
1050.115076000111
usercpu_time_millis_training
1032.8450340002746
usercpu_time_millis_training
1046.1627470003805
usercpu_time_millis_training
1069.3729149998035