Data
higgs

higgs

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  • artificial derived mf_less_than_80 OpenML100 physics study_123 uci
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Author: Daniel Whiteson, University of California Irvine Source: [UCI](https://archive.ics.uci.edu/ml/datasets/HIGGS) Please cite: Baldi, P., P. Sadowski, and D. Whiteson. Searching for Exotic Particles in High-energy Physics with Deep Learning. Nature Communications 5 (July 2, 2014). Higgs Boson detection data. The data has been produced using Monte Carlo simulations. The first 21 features (columns 2-22) are kinematic properties measured by the particle detectors in the accelerator. The last seven features are functions of the first 21 features; these are high-level features derived by physicists to help discriminate between the two classes. There is an interest in using deep learning methods to obviate the need for physicists to manually develop such features. The last 500,000 examples are used as a test set. Note: This is the UCI Higgs dataset, same as version 1, but it fixes the definition of the class attribute, which is categorical, not numeric. ### Attribute Information * The first column is the class label (1 for signal, 0 for background) * 21 low-level features (kinematic properties): lepton pT, lepton eta, lepton phi, missing energy magnitude, missing energy phi, jet 1 pt, jet 1 eta, jet 1 phi, jet 1 b-tag, jet 2 pt, jet 2 eta, jet 2 phi, jet 2 b-tag, jet 3 pt, jet 3 eta, jet 3 phi, jet 3 b-tag, jet 4 pt, jet 4 eta, jet 4 phi, jet 4 b-tag * 7 high-level features derived by physicists: m_jj, m_jjj, m_lv, m_jlv, m_bb, m_wbb, m_wwbb. For more detailed information about each feature see the original paper. Relevant Papers: Baldi, P., P. Sadowski, and D. Whiteson. Searching for Exotic Particles in High-energy Physics with Deep Learning. Nature Communications 5 (July 2, 2014).

29 features

class (target)nominal2 unique values
0 missing
lepton_pTnumeric13044 unique values
0 missing
lepton_etanumeric4919 unique values
0 missing
lepton_phinumeric6284 unique values
0 missing
missing_energy_magnitudenumeric92943 unique values
0 missing
missing_energy_phinumeric92777 unique values
0 missing
jet1ptnumeric20297 unique values
0 missing
jet1etanumeric5712 unique values
0 missing
jet1phinumeric6284 unique values
0 missing
jet1b-tagnumeric3 unique values
0 missing
jet2ptnumeric16512 unique values
0 missing
jet2etanumeric5804 unique values
0 missing
jet2phinumeric6284 unique values
0 missing
jet2b-tagnumeric3 unique values
0 missing
jet3ptnumeric12428 unique values
0 missing
jet3etanumeric5913 unique values
0 missing
jet3phinumeric6284 unique values
0 missing
jet3b-tagnumeric3 unique values
0 missing
jet4ptnumeric9389 unique values
0 missing
jet4etanumeric5974 unique values
0 missing
jet4phinumeric6284 unique values
1 missing
jet4b-tagnumeric3 unique values
1 missing
m_jjnumeric89763 unique values
1 missing
m_jjjnumeric68881 unique values
1 missing
m_lvnumeric45482 unique values
1 missing
m_jlvnumeric76814 unique values
1 missing
m_bbnumeric82416 unique values
1 missing
m_wbbnumeric83123 unique values
1 missing
m_wwbbnumeric85937 unique values
1 missing

107 properties

98050
Number of instances (rows) of the dataset.
29
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
9
Number of missing values in the dataset.
1
Number of instances with at least one value missing.
28
Number of numeric attributes.
1
Number of nominal attributes.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.32
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.32
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
0
First quartile of skewness among attributes of the numeric type.
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.35
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
1.36
Mean skewness among attributes of the numeric type.
0.49
First quartile of standard deviation of attributes of the numeric type.
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.34
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.77
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.32
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.32
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.45
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
52.86
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
-0.03
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1
Entropy of the target attribute values.
0.09
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
51827
Number of instances belonging to the most frequent class.
-1.86
Minimum kurtosis among attributes of the numeric type.
0.99
Second quartile (Median) of means among attributes of the numeric type.
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-0.01
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.32
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.39
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
63.89
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.6
Second quartile (Median) of skewness among attributes of the numeric type.
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
1.05
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
3.45
Percentage of binary attributes.
1
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.61
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
-0.01
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.39
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2
The maximum number of distinct values among attributes of the nominal type.
0.16
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
11.23
Third quartile of kurtosis among attributes of the numeric type.
0.5
Average class difference between consecutive instances.
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
5.98
Maximum skewness among attributes of the numeric type.
47.14
Percentage of instances belonging to the least frequent class.
96.55
Percentage of numeric attributes.
1
Third quartile of means among attributes of the numeric type.
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.61
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.35
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.4
Maximum standard deviation of attributes of the numeric type.
46223
Number of instances belonging to the least frequent class.
3.45
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.34
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.39
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
2.3
Third quartile of skewness among attributes of the numeric type.
0.32
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
7.9
Mean kurtosis among attributes of the numeric type.
0.4
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.2
First quartile of kurtosis among attributes of the numeric type.
1.01
Third quartile of standard deviation of attributes of the numeric type.
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.61
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.35
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.61
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0
First quartile of means among attributes of the numeric type.
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.34
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.39
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

12 tasks

11134 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - target_feature: class
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