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MiniBooNE

MiniBooNE

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Source: [UCI](https://archive.ics.uci.edu/ml/datasets/MiniBooNE+particle+identification#) Byron Roe (byronroe '@' umich.edu) Department of Physics University of Michigan Ann Arbor, MI 48109 Data Set Information: 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. Attribute Information: 50 particle ID variables (real) for each event. Relevant Papers: B. Roe et al., 'Boosted Decision Trees, an Alternative to Artificial Neural Networks' <[Web Link](https://arxiv.org/abs/physics/0408124)>, arXiv:physics/0408124, Nucl. Instrum. Meth. A543, 577 (2005).

51 features

signal (target)nominal2 unique values
0 missing
ParticleID_37numeric109146 unique values
0 missing
ParticleID_26numeric107033 unique values
0 missing
ParticleID_27numeric69277 unique values
0 missing
ParticleID_28numeric93139 unique values
0 missing
ParticleID_29numeric124781 unique values
0 missing
ParticleID_30numeric115505 unique values
0 missing
ParticleID_31numeric101816 unique values
0 missing
ParticleID_32numeric114883 unique values
0 missing
ParticleID_33numeric105461 unique values
0 missing
ParticleID_34numeric117368 unique values
0 missing
ParticleID_35numeric100895 unique values
0 missing
ParticleID_36numeric125172 unique values
0 missing
ParticleID_24numeric82961 unique values
0 missing
ParticleID_38numeric77900 unique values
0 missing
ParticleID_39numeric114710 unique values
0 missing
ParticleID_40numeric81089 unique values
0 missing
ParticleID_41numeric123144 unique values
0 missing
ParticleID_42numeric124144 unique values
0 missing
ParticleID_43numeric119342 unique values
0 missing
ParticleID_44numeric13947 unique values
0 missing
ParticleID_45numeric105274 unique values
0 missing
ParticleID_46numeric125393 unique values
0 missing
ParticleID_47numeric116901 unique values
0 missing
ParticleID_48numeric122183 unique values
0 missing
ParticleID_49numeric93277 unique values
0 missing
ParticleID_12numeric121900 unique values
0 missing
ParticleID_0numeric111368 unique values
0 missing
ParticleID_1numeric110398 unique values
0 missing
ParticleID_2numeric110446 unique values
0 missing
ParticleID_3numeric94548 unique values
0 missing
ParticleID_4numeric16348 unique values
0 missing
ParticleID_5numeric94447 unique values
0 missing
ParticleID_6numeric107776 unique values
0 missing
ParticleID_7numeric98507 unique values
0 missing
ParticleID_8numeric68287 unique values
0 missing
ParticleID_9numeric88422 unique values
0 missing
ParticleID_10numeric68644 unique values
0 missing
ParticleID_11numeric114594 unique values
0 missing
ParticleID_25numeric125875 unique values
0 missing
ParticleID_13numeric118898 unique values
0 missing
ParticleID_14numeric94193 unique values
0 missing
ParticleID_15numeric118540 unique values
0 missing
ParticleID_16numeric117028 unique values
0 missing
ParticleID_17numeric118782 unique values
0 missing
ParticleID_18numeric40019 unique values
0 missing
ParticleID_19numeric115886 unique values
0 missing
ParticleID_20numeric124096 unique values
0 missing
ParticleID_21numeric39401 unique values
0 missing
ParticleID_22numeric121281 unique values
0 missing
ParticleID_23numeric113276 unique values
0 missing
Show all 51 features

62 properties

NumberOfInstances
130064
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.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
PercentageOfBinaryFeatures
1.96
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
98.04
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
1.96
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
272.32
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
-3.42
First quartile of means among attributes of the numeric type.
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Quartile1SkewnessOfNumericAtts
-16.58
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
59.83
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
272.88
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
-2.82
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
-16.58
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
59.88
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
272.93
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
0.85
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
-16.55
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
60.17
Third quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
1
Average class difference between consecutive instances.
MeanMeansOfNumericAtts
38.39
Mean of means among attributes of the numeric type.
ClassEntropy
0.86
Entropy of the target attribute values.
Dimensionality
0
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MajorityClassPercentage
71.94
Percentage of instances belonging to the most frequent class.
MajorityClassSize
93565
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
130062.96
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
933.04
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
2
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
360.64
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
44367.69
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
2834.99
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
1
Number of binary attributes.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
2
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
-6.83
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
965.8
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
8.79
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
-25.23
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-16.58
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
59.66
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
28.06
Percentage of instances belonging to the least frequent class.
MinorityClassSize
36499
Number of instances belonging to the least frequent class.
Show all 62 properties

15 tasks

Supervised Classification on MiniBooNE
12 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: area_under_roc_curve - target_feature: signal
Supervised Classification on MiniBooNE
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: signal
Supervised Classification on MiniBooNE
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: signal
Supervised Classification on MiniBooNE
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: signal
Supervised Classification on MiniBooNE
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: signal
Supervised Classification on MiniBooNE
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: signal
Clustering on MiniBooNE
0 runs - estimation_procedure: 50 times Clustering
Clustering on MiniBooNE
0 runs - estimation_procedure: 50 times Clustering
Clustering on MiniBooNE
0 runs - estimation_procedure: 50 times Clustering
Clustering on MiniBooNE
0 runs - estimation_procedure: 50 times Clustering
Clustering on MiniBooNE
0 runs - estimation_procedure: 50 times Clustering
Clustering on MiniBooNE
0 runs - estimation_procedure: 50 times Clustering
Clustering on MiniBooNE
0 runs - estimation_procedure: 50 times Clustering
Clustering on MiniBooNE
0 runs - estimation_procedure: 50 times Clustering
Clustering on MiniBooNE
0 runs - estimation_procedure: 50 times Clustering
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