Data
MiniBooNE

MiniBooNE

active ARFF Public Domain (CC0) Visibility: public Uploaded 21-06-2022 by Leo Grin
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Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on numerical features" benchmark. Original description: This dataset is taken from the MiniBooNE experiment and is used to distinguish electron neutrinos (signal) from muon neutrinos (background). This dataset is ordered. It first contains all signal observations, and then background observations. B. Roe et al., 'Boosted Decision Trees, an Alternative to Artificial Neural Networks' arXiv:physics/0408124, Nucl. Instrum. Meth. A543, 577 (2005).

51 features

signal (target)nominal2 unique values
0 missing
ParticleID_0numeric66533 unique values
0 missing
ParticleID_1numeric67643 unique values
0 missing
ParticleID_2numeric68415 unique values
0 missing
ParticleID_3numeric58686 unique values
0 missing
ParticleID_4numeric14370 unique values
0 missing
ParticleID_5numeric57355 unique values
0 missing
ParticleID_6numeric63117 unique values
0 missing
ParticleID_7numeric61712 unique values
0 missing
ParticleID_8numeric48776 unique values
0 missing
ParticleID_9numeric58271 unique values
0 missing
ParticleID_10numeric48272 unique values
0 missing
ParticleID_11numeric67660 unique values
0 missing
ParticleID_12numeric69994 unique values
0 missing
ParticleID_13numeric68765 unique values
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ParticleID_14numeric61900 unique values
0 missing
ParticleID_15numeric69144 unique values
0 missing
ParticleID_16numeric69505 unique values
0 missing
ParticleID_17numeric69446 unique values
0 missing
ParticleID_18numeric32956 unique values
0 missing
ParticleID_19numeric67121 unique values
0 missing
ParticleID_20numeric70861 unique values
0 missing
ParticleID_21numeric31894 unique values
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ParticleID_22numeric70074 unique values
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ParticleID_23numeric67485 unique values
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ParticleID_24numeric51949 unique values
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ParticleID_25numeric71640 unique values
0 missing
ParticleID_26numeric65748 unique values
0 missing
ParticleID_27numeric49536 unique values
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ParticleID_28numeric58970 unique values
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ParticleID_29numeric71316 unique values
0 missing
ParticleID_30numeric68350 unique values
0 missing
ParticleID_31numeric60263 unique values
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ParticleID_32numeric68667 unique values
0 missing
ParticleID_33numeric64752 unique values
0 missing
ParticleID_34numeric67541 unique values
0 missing
ParticleID_35numeric63227 unique values
0 missing
ParticleID_36numeric71147 unique values
0 missing
ParticleID_37numeric66252 unique values
0 missing
ParticleID_38numeric52482 unique values
0 missing
ParticleID_39numeric67579 unique values
0 missing
ParticleID_40numeric54702 unique values
0 missing
ParticleID_41numeric70838 unique values
0 missing
ParticleID_42numeric71136 unique values
0 missing
ParticleID_43numeric69455 unique values
0 missing
ParticleID_44numeric9407 unique values
0 missing
ParticleID_45numeric63713 unique values
0 missing
ParticleID_46numeric71410 unique values
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ParticleID_47numeric68479 unique values
0 missing
ParticleID_48numeric70728 unique values
0 missing
ParticleID_49numeric59483 unique values
0 missing
Show all 51 features

19 properties

NumberOfInstances
72998
Number of instances (rows) of the dataset.
NumberOfFeatures
51
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
50
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
PercentageOfBinaryFeatures
1.96
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
AutoCorrelation
1
Average class difference between consecutive instances.
PercentageOfNumericFeatures
98.04
Percentage of numeric attributes.
Dimensionality
0
Number of attributes divided by the number of instances.
PercentageOfSymbolicFeatures
1.96
Percentage of nominal attributes.
MajorityClassPercentage
50
Percentage of instances belonging to the most frequent class.
MajorityClassSize
36499
Number of instances belonging to the most frequent class.
MinorityClassPercentage
50
Percentage of instances belonging to the least frequent class.
MinorityClassSize
36499
Number of instances belonging to the least frequent class.
NumberOfBinaryFeatures
1
Number of binary attributes.
Show all 19 properties

1 tasks

Supervised Classification on MiniBooNE
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: signal
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