6040817
1
Jan van Rijn
9954
Supervised Classification
6952
sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.svm.classes.SVC)(1)
4075498
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
C
549.4264198404019
6953
cache_size
200
6953
class_weight
null
6953
coef0
-0.10540573726025548
6953
decision_function_shape
null
6953
degree
3
6953
gamma
0.05765092375524838
6953
kernel
"sigmoid"
6953
max_iter
-1
6953
probability
true
6953
random_state
58613
6953
shrinking
false
6953
tol
7.89373514574744e-05
6953
verbose
false
6953
openml-pimp
openml-python
Sklearn_0.18.1.
1491
one-hundred-plants-margin
https://www.openml.org/data/download/1592283/phpCsX3fx
-1
13004671
description
https://api.openml.org/data/download/13004671/description.xml
-1
13004672
predictions
https://api.openml.org/data/download/13004672/predictions.arff
area_under_roc_curve
0.994649226641414 [0.992898,0.999684,1,1,0.999566,0.994673,0.99925,1,0.993766,0.999921,0.998382,0.999605,0.999329,0.976444,0.999763,1,1,0.981889,0.976405,0.97021,0.992385,0.999803,0.994081,1,0.995581,0.971473,0.972854,0.998737,0.998619,1,0.999803,1,0.999329,0.989938,0.999211,0.998816,0.985085,0.97017,0.999803,1,0.998106,0.998501,1,0.998816,0.999171,0.998935,0.999527,0.996686,0.998658,0.996133,0.994673,0.996567,0.998146,0.983704,0.997751,0.971275,1,0.999684,0.991911,0.984414,0.999014,0.998935,0.994713,0.997909,0.999171,0.992582,0.985953,0.999882,0.993056,0.993253,0.989978,0.997751,0.991319,1,0.982481,0.999645,0.999842,0.988676,1,0.999487,0.994515,0.997672,0.999369,0.994871,0.99779,0.999763,1,0.998422,0.999408,0.999329,0.992543,0.999408,0.998343,0.996528,0.999487,0.997514,0.994555,0.997475,0.958767,0.995384]
average_cost
0
f_measure
0.7751972484169525 [0.705882,0.857143,1,1,0.9375,0.5,0.9375,1,0.588235,0.896552,0.866667,0.941176,0.933333,0.625,1,0.969697,1,0.258065,0.222222,0.176471,0.5,0.9375,0.682927,1,0.8125,0.382979,0.277778,0.8125,0.903226,0.896552,0.967742,0.967742,0.875,0.470588,0.933333,0.6875,0.375,0.357143,0.848485,0.967742,0.647059,0.9375,0.933333,0.8125,0.9375,0.882353,0.866667,0.83871,0.789474,0.758621,0.823529,0.758621,0.866667,0.8,0.787879,0.166667,1,0.903226,0.651163,0.666667,0.774194,0.875,0.742857,0.875,0.903226,0.6,0.285714,1,0.75,0.764706,0.62069,0.827586,0.545455,0.969697,0.83871,0.967742,0.903226,0.5,1,0.9375,0.709677,0.933333,0.909091,0.347826,0.882353,0.875,0.967742,0.857143,0.909091,1,0.740741,0.827586,0.848485,0.896552,0.9375,0.8,0.833333,0.666667,0.263158,0.733333]
kappa
0.7720959595959597
kb_relative_information_score
956.3261835226582
mean_absolute_error
0.016426197989538097
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
0.7861669699713566 [0.666667,0.789474,1,1,0.9375,0.583333,0.9375,1,0.555556,1,0.928571,0.888889,1,0.625,1,0.941176,1,0.266667,0.272727,0.166667,0.5,0.9375,0.56,1,0.8125,0.290323,0.25,0.8125,0.933333,1,1,1,0.875,0.444444,1,0.6875,0.375,0.416667,0.823529,1,0.611111,0.9375,1,0.8125,0.9375,0.833333,0.928571,0.866667,0.681818,0.846154,0.777778,0.846154,0.928571,0.857143,0.764706,0.25,1,0.933333,0.518519,0.6,0.8,0.875,0.684211,0.875,0.933333,0.642857,0.333333,1,0.75,0.722222,0.692308,0.923077,0.529412,0.941176,0.866667,1,0.933333,0.45,1,0.9375,0.733333,1,0.882353,0.571429,0.833333,0.875,1,1,0.882353,1,0.909091,0.923077,0.823529,1,0.9375,0.736842,0.75,0.647059,0.227273,0.785714]
predictive_accuracy
0.774375
prior_entropy
6.6438561897747395
recall
0.774375 [0.75,0.9375,1,1,0.9375,0.4375,0.9375,1,0.625,0.8125,0.8125,1,0.875,0.625,1,1,1,0.25,0.1875,0.1875,0.5,0.9375,0.875,1,0.8125,0.5625,0.3125,0.8125,0.875,0.8125,0.9375,0.9375,0.875,0.5,0.875,0.6875,0.375,0.3125,0.875,0.9375,0.6875,0.9375,0.875,0.8125,0.9375,0.9375,0.8125,0.8125,0.9375,0.6875,0.875,0.6875,0.8125,0.75,0.8125,0.125,1,0.875,0.875,0.75,0.75,0.875,0.8125,0.875,0.875,0.5625,0.25,1,0.75,0.8125,0.5625,0.75,0.5625,1,0.8125,0.9375,0.875,0.5625,1,0.9375,0.6875,0.875,0.9375,0.25,0.9375,0.875,0.9375,0.75,0.9375,1,0.625,0.75,0.875,0.8125,0.9375,0.875,0.9375,0.6875,0.3125,0.6875]
relative_absolute_error
0.8296059590675792
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.0843716472326461
root_relative_squared_error
0.847966960045195
total_cost
0
area_under_roc_curve
0.9936390414775896 [0.974843,1,1,1,1,1,1,1,0.993671,1,1,0.993671,1,0.952532,1,1,1,0.996835,0.96519,0.987421,0.981132,1,1,1,0.993711,0.990506,0.977848,1,1,1,1,1,0.996835,0.981013,0.990506,0.987421,1,0.993711,1,1,1,0.993711,1,1,1,1,1,1,1,0.974684,0.996835,1,1,0.990506,1,0.981132,1,1,1,0.996835,1,1,1,0.996835,1,1,1,1,1,0.918239,0.977848,1,1,1,1,1,1,0.974684,1,1,1,1,1,1,0.993671,1,1,1,0.996835,1,0.996835,1,1,0.990506,1,0.984177,0.993711,0.990506,0.889241,1]
area_under_roc_curve
0.9938360799299417 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.924051,1,1,1,0.968354,0.993671,0.981132,1,1,1,1,1,0.968354,0.96519,1,1,1,1,1,0.996835,1,0.996835,0.993711,0.993711,0.968553,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.962264,1,1,1,0.952532,1,1,0.996835,1,1,0.993671,0.996835,1,0.937107,1,0.987342,1,1,1,1,1,1,0.984177,1,0.993671,0.977848,1,1,1,0.990506,1,1,1,1,1,0.974684,1,1,0.984177,1,1,1,0.990506,0.968354,0.987421]
area_under_roc_curve
0.9942368740546133 [0.996835,1,1,1,1,0.993671,0.993711,1,0.990506,1,1,1,1,0.996835,1,1,1,0.962025,0.981132,0.943038,0.974843,1,0.974684,1,1,0.971519,0.974684,0.993711,1,1,1,1,1,0.987342,1,1,0.987421,0.893082,1,1,0.993671,0.993711,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.987342,1,1,0.981132,0.968354,1,1,1,0.968553,0.996835,0.996835,1,1,0.993711,0.996835,0.984177,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,0.996835,0.993671,1,1,1,0.930818,1]
area_under_roc_curve
0.9936833253721838 [0.996835,1,1,1,1,0.990506,1,1,0.987342,1,0.996835,1,1,1,1,1,1,1,0.91195,0.971519,1,1,0.996835,1,0.974843,0.977848,0.952532,1,1,1,1,1,1,0.993671,1,1,0.993711,0.949686,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.96519,1,0.958861,1,1,0.993711,0.987342,1,1,1,1,0.996835,0.996835,0.971519,1,1,0.990506,0.981013,0.996835,0.974843,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,0.993711,0.984177,0.990506,0.893082,0.974684]
area_under_roc_curve
0.9951797727091792 [1,1,1,1,1,1,1,1,0.987421,1,0.993671,1,1,0.937107,1,1,1,0.996835,1,0.984177,1,1,0.996835,1,0.990506,0.987342,0.977848,1,1,1,1,1,1,0.981013,1,1,0.977848,0.962025,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,0.974843,1,0.993711,0.987342,0.996835,1,0.996835,0.996835,1,1,1,1,1,1,1,0.987421,1,0.996835,0.990506,1,1,1,1,0.911392,1,0.993671,0.987342,1,1,1,1,1,0.990506,0.987421,1,1,1,1,1,1,0.996835,1,1,0.993671,1,1,1,0.993711,1]
area_under_roc_curve
0.9949404406496296 [0.962025,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,0.990506,0.993711,0.946203,1,1,0.990506,1,0.993671,0.971519,0.981013,1,1,1,1,1,1,0.993671,1,0.996835,0.984177,0.977848,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,0.962025,1,1,0.993671,1,0.993671,1,0.987421,1,1,0.990506,1,1,1,1,0.996835,0.990506,1,1,0.987342,1,1,0.984177,1,1,0.993711,0.993671,1,0.987342,1,1,1,1,1,1,0.968553,1,1,1,1,1,1,1,0.962264,0.987342]
area_under_roc_curve
0.9947914178807418 [0.996835,1,1,1,1,0.996835,1,1,0.993711,1,1,1,0.993671,1,1,1,1,0.993711,0.990506,0.962025,0.993671,1,1,1,1,0.861635,0.899371,0.993671,1,1,0.996835,1,1,0.993711,1,1,0.958861,0.971519,1,1,1,1,1,1,0.987421,1,1,0.990506,1,1,0.993711,1,1,1,0.996835,0.993671,1,1,0.987342,1,1,0.996835,1,0.996835,1,0.968553,1,0.996835,0.993671,1,1,1,0.996835,1,1,1,1,0.981132,1,1,0.981013,1,1,1,1,1,1,1,1,1,1,1,0.987342,1,1,1,0.990506,1,0.984177,1]
area_under_roc_curve
0.9961326228007327 [1,1,1,1,1,1,1,1,0.987421,1,0.987421,1,1,1,1,1,1,0.949686,0.968354,1,0.993671,1,0.993671,1,0.993671,0.981132,0.987421,1,0.993711,1,1,1,1,1,1,1,0.984177,0.96519,1,1,1,1,1,1,1,1,1,0.990506,1,0.993711,0.962264,0.996835,1,1,1,0.962025,1,1,0.987342,1,1,1,1,1,1,0.974843,0.962264,1,0.996835,1,1,1,0.996835,1,1,1,1,0.993711,1,1,0.996835,1,0.996835,1,1,1,1,1,1,1,0.993711,1,1,1,1,0.993671,0.996835,1,0.993671,1]
area_under_roc_curve
0.995616143221081 [1,1,1,1,1,0.993711,0.996835,1,1,1,1,1,1,1,1,1,1,0.993711,0.990506,0.987421,0.996835,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,0.987342,0.974684,1,1,0.993711,1,1,0.990506,1,0.987342,1,0.993671,0.993711,0.993711,0.990506,0.996835,1,0.936709,0.996835,0.918239,1,1,0.993671,1,0.996835,1,1,0.996835,1,0.993711,0.993671,1,0.990506,1,1,0.987421,0.955696,1,1,1,1,0.993711,1,0.996835,1,1,1,0.981132,1,1,1,1,1,1,0.996835,1,1,0.996835,1,0.993671,1,1,0.987342,1]
area_under_roc_curve
0.9965264509195129 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.990506,1,1,1,0.937107,0.974684,0.981132,0.977848,1,1,1,1,0.987421,0.993711,1,0.996835,1,1,1,1,1,1,1,0.996835,0.996835,1,1,1,0.996835,1,1,1,1,1,1,1,1,0.996835,1,1,0.987342,1,0.987421,1,1,0.990506,0.990506,1,1,0.984177,0.996835,0.993711,0.993711,0.987342,1,1,0.993711,1,1,0.984177,1,0.996835,0.996835,1,1,1,1,1,0.974843,1,0.981132,1,1,1,0.996835,1,0.996835,0.993671,1,1,0.996835,1,0.996835,0.987421,0.996835,1,1]
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
0.760006315623273
kappa
0.785226420308737
kappa
0.78520097923083
kappa
0.760006315623273
kappa
0.7599210266535044
kappa
0.7473451502112036
kappa
0.7789095503178175
kappa
0.772592680326898
kappa
0.7726285872182529
kappa
0.7979000552617037
kb_relative_information_score
95.77090411000704
kb_relative_information_score
94.22540851317959
kb_relative_information_score
95.56639612495896
kb_relative_information_score
95.10103585537937
kb_relative_information_score
95.25284528432542
kb_relative_information_score
93.93383155857202
kb_relative_information_score
98.08021907668018
kb_relative_information_score
96.26468502379721
kb_relative_information_score
95.75334565483342
kb_relative_information_score
96.37751232092563
mean_absolute_error
0.01635164940875026
mean_absolute_error
0.01654779890799628
mean_absolute_error
0.016447361139123776
mean_absolute_error
0.01646641535507917
mean_absolute_error
0.016506038738180316
mean_absolute_error
0.016619910124378506
mean_absolute_error
0.016139413083270285
mean_absolute_error
0.016383379242531304
mean_absolute_error
0.016441257412041933
mean_absolute_error
0.01635875648402927
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
0.7625
predictive_accuracy
0.7875
predictive_accuracy
0.7875
predictive_accuracy
0.7625
predictive_accuracy
0.7625
predictive_accuracy
0.75
predictive_accuracy
0.78125
predictive_accuracy
0.775
predictive_accuracy
0.775
predictive_accuracy
0.8
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
0.7625 [1,0.5,1,1,1,1,1,1,0,1,0.5,1,1,0,1,1,1,0,0.5,1,1,1,1,1,0,0.5,0,1,1,0,1,1,0.5,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0,0.5,1,0.5,1,1,1,0.5,1,1,1,1,1,1,0,1,0.5,1,0.5,0.5,1,0.5,1,1,0.5,1,0.5,1,1,0,1]
recall
0.7875 [1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,0.5,1,1,1,0,0.5,0,1,1,1,1,1,0,0,0.5,1,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,0,1,1,1,0.5,1,1,1,1,1,0.5,0.5,1,0,1,0.5,1,0,1,1,1,0.5,1,1,0.5,0,1,1,0,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0.5,0]
recall
0.7875 [0.5,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,1,0.5,0,0,0,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,0,1,0.5,0,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0,1,1,1,0,1,1,0.5,0,1,1,0,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,0,1]
recall
0.7625 [1,1,1,1,0.5,0.5,1,1,0,0.5,1,1,1,0.5,1,1,1,0.5,0,0.5,0,1,1,1,0,0.5,0,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,0,1,1,1,0.5,1,1,1,0,0.5,0,0.5,1,1,0.5,0,0.5,0,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,0.5,0,0]
recall
0.7625 [0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,0,0,1,1,1,0.5,0,0,1,1,1,1,1,1,0,1,1,0.5,1,1,0.5,0,1,1,0.5,0,1,1,1,1,1,0,1,0,1,1,1,1,0,0,0.5,1,0.5,0,1,0.5,1,1,1,1,1,1,0.5,1,0,1,0.5,0.5,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,0,1,1,0,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1]
recall
0.75 [0.5,1,1,1,1,0,1,1,1,0.5,0.5,1,1,1,1,1,1,0,1,0.5,1,0.5,1,1,1,1,0.5,0,1,0.5,1,1,1,0.5,1,0.5,0,0,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,0,1,1,0.5,1,0,1,1,1,1,0,1,1,1,1,0.5,0,1,1,0.5,1,1,0.5,1,1,0,0.5,1,0.5,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0,0]
recall
0.78125 [1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,1,1,1,1,0,0,0.5,1,1,1,1,1,1,1,1,0,0,1,1,1,0.5,1,1,0,1,0,0.5,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,0.5,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,0,1,1,1,0,1,1,1,1,0,1,1,1,1,1,0,1]
recall
0.775 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,0,0,0,0.5,1,0.5,1,0.5,1,1,1,0,1,0.5,1,1,1,1,1,0,1,1,1,0,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0,1,0.5,1,0,1,1,0,1,1,1,1,1,1,0,0.5,1]
recall
0.775 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0,0,1,0,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,0.5,0.5,0,1,0,1,1,0,1,1,1,1,1,0.5,1,0.5,1,1,0,0,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,0.5,1,0.5,1,0.5,1,1,0.5,1]
recall
0.8 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,0,0,0.5,1,1,1,1,0,0,1,0.5,1,1,0.5,1,1,1,0,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,0.5,1,0,1,1,1,1,1,1,0,1,1,1,0,1,0.5,1,0,1,0.5,1,0.5,1,1,1,1,1,1,0,1,0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1]
relative_absolute_error
0.8258408792298098
relative_absolute_error
0.8357474195957704
relative_absolute_error
0.8306748050062499
relative_absolute_error
0.8316371391454112
relative_absolute_error
0.8336383201101155
relative_absolute_error
0.8393894002211353
relative_absolute_error
0.8151218728924373
relative_absolute_error
0.8274433960874383
relative_absolute_error
0.8303665359617124
relative_absolute_error
0.8261998224257193
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
0.08413554176617105
root_mean_squared_error
0.08479558325934364
root_mean_squared_error
0.08445632739891845
root_mean_squared_error
0.08453360048042775
root_mean_squared_error
0.08469674572763562
root_mean_squared_error
0.08539766324558604
root_mean_squared_error
0.08303605191163763
root_mean_squared_error
0.08409890884829962
root_mean_squared_error
0.08443405762802794
root_mean_squared_error
0.08411209955936855
root_relative_squared_error
0.8455940108232268
root_relative_squared_error
0.8522276774260129
root_relative_squared_error
0.8488180277382569
root_relative_squared_error
0.8495946514284414
root_relative_squared_error
0.8512343228566785
root_relative_squared_error
0.8582788089658565
root_relative_squared_error
0.8345437220102275
root_relative_squared_error
0.8452258361457862
root_relative_squared_error
0.8485942081194355
root_relative_squared_error
0.8453584077801304
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
1839.7364049997122
usercpu_time_millis
1682.8481040001861
usercpu_time_millis
1684.5686920000844
usercpu_time_millis
1691.8423790002635
usercpu_time_millis
1690.24947600019
usercpu_time_millis
1693.0582149998372
usercpu_time_millis
1704.6339290000105
usercpu_time_millis
1704.2804569996406
usercpu_time_millis
1705.3693589996328
usercpu_time_millis
1699.5222610003111
usercpu_time_millis_testing
157.8812719999405
usercpu_time_millis_testing
159.73887000018294
usercpu_time_millis_testing
155.93503999980385
usercpu_time_millis_testing
156.875594000212
usercpu_time_millis_testing
157.15107799996986
usercpu_time_millis_testing
156.4255260000209
usercpu_time_millis_testing
159.41321200034508
usercpu_time_millis_testing
160.54710499975045
usercpu_time_millis_testing
159.0249229998335
usercpu_time_millis_testing
159.06423900014488
usercpu_time_millis_training
1681.8551329997717
usercpu_time_millis_training
1523.1092340000032
usercpu_time_millis_training
1528.6336520002806
usercpu_time_millis_training
1534.9667850000515
usercpu_time_millis_training
1533.0983980002202
usercpu_time_millis_training
1536.6326889998163
usercpu_time_millis_training
1545.2207169996655
usercpu_time_millis_training
1543.7333519998901
usercpu_time_millis_training
1546.3444359997993
usercpu_time_millis_training
1540.4580220001662