Data
TuningSVMs

TuningSVMs

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Author: Rafael G. Mantovani, Edesio Alcobaça, André L. D. Rossi, Joaquin Vanschoren, André C. P. L. F. de Carvalho Source: "A meta-learning recommender system for hyperparameter tuning: predicting when tuning improves SVM classifiers" - Information Sciences, volume 501, 2019. Please cite: 10.1016/j.ins.2019.06.005 This is a meta-dataset which describes the SVM hyperparameter tuning problem. The target attribute indicates whether tuning is required or default hyperparameter values are enough to each dataset (row). Targets were defined using a statistical labelling rule comparing the predictive performance of models induced with defaults values and tuned ones. In this version of the dataset, the labelling rule was set with 95% confidence.

91 features

Class (target)nominal2 unique values
0 missing
datasets (row identifier)nominal156 unique values
0 missing
diff.svm.df.lmnumeric153 unique values
0 missing
diff.svm.nbnumeric155 unique values
0 missing
diff.svm.stumpnumeric155 unique values
0 missing
diff.svm.nnnumeric153 unique values
0 missing
diff.nn.lmnumeric152 unique values
0 missing
diff.nn.stumpnumeric151 unique values
0 missing
diff.nn.nbnumeric149 unique values
0 missing
diff.nb.stumpnumeric151 unique values
0 missing
diff.nb.lmnumeric154 unique values
0 missing
diff.stump.lmnumeric155 unique values
0 missing
simple.classesnumeric12 unique values
0 missing
simple.attributesnumeric51 unique values
0 missing
simple.numericnumeric46 unique values
0 missing
simple.nominalnumeric21 unique values
0 missing
simple.samplesnumeric129 unique values
0 missing
simple.dimensionalitynumeric140 unique values
0 missing
simple.numeric_ratenumeric32 unique values
0 missing
simple.nominal_ratenumeric32 unique values
0 missing
simple.symbols_minnumeric11 unique values
0 missing
simple.symbols_maxnumeric22 unique values
0 missing
simple.symbols_meannumeric45 unique values
0 missing
simple.symbols_sdnumeric40 unique values
0 missing
simple.symbols_sumnumeric44 unique values
0 missing
simple.class_prob_minnumeric139 unique values
0 missing
simple.class_prob_maxnumeric141 unique values
0 missing
simple.class_prob_meannumeric12 unique values
0 missing
simple.class_prob_sdnumeric131 unique values
0 missing
statistical.skewnessnumeric136 unique values
0 missing
statistical.skewness_prepnumeric151 unique values
0 missing
statistical.kurtosisnumeric136 unique values
0 missing
statistical.kurtosis_prepnumeric151 unique values
0 missing
statistical.abs_cornumeric151 unique values
0 missing
statistical.cancor_1numeric144 unique values
0 missing
statistical.fract_1numeric57 unique values
0 missing
inftheo.class_entropynumeric140 unique values
0 missing
inftheo.normalized_class_entropynumeric130 unique values
0 missing
inftheo.attribute_entropynumeric149 unique values
0 missing
inftheo.normalized_attribute_entropynumeric149 unique values
0 missing
inftheo.joint_entropynumeric146 unique values
0 missing
inftheo.mutual_informationnumeric151 unique values
0 missing
inftheo.equivalent_attributesnumeric151 unique values
0 missing
inftheo.noise_signal_rationumeric151 unique values
0 missing
modelbased.nodesnumeric24 unique values
0 missing
modelbased.leaves.nodes_per_attributenumeric24 unique values
0 missing
modelbased.nodes_per_instancenumeric97 unique values
0 missing
modelbased.leaf_corrobationnumeric140 unique values
0 missing
modelbased.level_minnumeric24 unique values
0 missing
modelbased.level_maxnumeric2 unique values
0 missing
modelbased.level_meannumeric9 unique values
0 missing
modelbased.level_sdnumeric38 unique values
0 missing
modelbased.branch_minnumeric47 unique values
0 missing
modelbased.branch_maxnumeric4 unique values
0 missing
modelbased.branch_meannumeric15 unique values
0 missing
modelbased.branch_sdnumeric72 unique values
0 missing
modelbased.attribute_minnumeric78 unique values
0 missing
modelbased.attribute_maxnumeric4 unique values
0 missing
modelbased.attribute_meannumeric9 unique values
0 missing
modelbased.attribute_sdnumeric97 unique values
0 missing
modelbased.NAnumeric122 unique values
0 missing
landmarking.naive_bayesnumeric149 unique values
0 missing
landmarking.stump_minnumeric146 unique values
0 missing
landmarking.stump_maxnumeric146 unique values
0 missing
landmarking.stump_meannumeric151 unique values
0 missing
landmarking.stump_sdnumeric122 unique values
0 missing
landmarking.stump_min_gainnumeric146 unique values
0 missing
landmarking.stump_randomnumeric145 unique values
0 missing
landmarking.nn_1numeric146 unique values
0 missing
dcomp.f1numeric148 unique values
0 missing
dcomp.f1vnumeric152 unique values
0 missing
dcomp.f2numeric86 unique values
0 missing
dcomp.f3numeric126 unique values
0 missing
dcomp.f4numeric122 unique values
0 missing
dcomp.l1numeric143 unique values
0 missing
dcomp.l2numeric112 unique values
0 missing
dcomp.l3numeric51 unique values
0 missing
dcomp.n1numeric137 unique values
0 missing
dcomp.n2numeric142 unique values
0 missing
dcomp.n3numeric126 unique values
0 missing
dcomp.n4numeric133 unique values
0 missing
dcomp.t1numeric48 unique values
0 missing
dcomp.t2numeric145 unique values
0 missing
cnet.edgesnumeric151 unique values
0 missing
cnet.degreenumeric152 unique values
0 missing
cnet.densitynumeric152 unique values
0 missing
cnet.maxCompnumeric134 unique values
0 missing
cnet.closenessnumeric63 unique values
0 missing
cnet.betweennessnumeric152 unique values
0 missing
cnet.clsCoefnumeric152 unique values
0 missing
cnet.hubsnumeric151 unique values
0 missing
cnet.avgPathnumeric152 unique values
0 missing

62 properties

156
Number of instances (rows) of the dataset.
91
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
90
Number of numeric attributes.
1
Number of nominal attributes.
0.1
First quartile of skewness among attributes of the numeric type.
8717.73
Mean of means among attributes of the numeric type.
0.22
First quartile of standard deviation of attributes of the numeric type.
0.55
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
0.95
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
2.57
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.58
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
0.77
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2.21
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
62.82
Percentage of instances belonging to the most frequent class.
23393.51
Mean standard deviation of attributes of the numeric type.
1.29
Second quartile (Median) of skewness among attributes of the numeric type.
98
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
1.1
Percentage of binary attributes.
0.36
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.22
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
154.95
Maximum kurtosis among attributes of the numeric type.
-0.13
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
16.52
Third quartile of kurtosis among attributes of the numeric type.
779933.33
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
98.9
Percentage of numeric attributes.
4.18
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
1.1
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
-6.06
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
3.53
Third quartile of skewness among attributes of the numeric type.
12.43
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
0.02
First quartile of kurtosis among attributes of the numeric type.
5.41
Third quartile of standard deviation of attributes of the numeric type.
2096900.37
Maximum standard deviation of attributes of the numeric type.
37.18
Percentage of instances belonging to the least frequent class.
58
Number of instances belonging to the least frequent class.
0.23
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
18.12
Mean kurtosis among attributes of the numeric type.

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