Data
higgs

higgs

active ARFF Publicly available Visibility: public Uploaded 16-02-2016 by Hilda Fabiola Bernard
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Author: Daniel Whiteson daniel'@'uci.edu", Assistant Professor, Physics, Univ. 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). Data Set Information: 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. Benchmark results using Bayesian Decision Trees from a standard physics package and 5-layer neural networks are presented in the original paper. The last 500,000 examples are used as a test set. Attribute Information: The first column is the class label (1 for signal, 0 for background), followed by the 28 features (21 low-level features then 7 high-level features): 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, 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)numeric2 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

62 properties

98050
Number of instances (rows) of the dataset.
29
Number of attributes (columns) of the dataset.
0
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.
29
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of binary attributes.
1
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.99
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
63.89
Maximum kurtosis among attributes of the numeric type.
-0.01
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
10.2
Third quartile of kurtosis among attributes of the numeric type.
1.05
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
100
Percentage of numeric attributes.
1
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
The maximum number of distinct values among attributes of the nominal type.
-0.11
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
2.2
Third quartile of skewness among attributes of the numeric type.
5.98
Maximum skewness among attributes of the numeric type.
0.16
Minimum standard deviation of attributes of the numeric type.
-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.
1.4
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
-0
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
Number of instances belonging to the least frequent class.
First quartile of mutual information between the nominal attributes and the target attribute.
7.56
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
0
First quartile of skewness among attributes of the numeric type.
0.6
Mean of means among attributes of the numeric type.
0.49
First quartile of standard deviation of attributes of the numeric type.
0.5
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
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.
-0.03
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
0.99
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.
1.31
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Percentage of instances belonging to the most frequent class.
0.76
Mean standard deviation of attributes of the numeric type.
0.43
Second quartile (Median) of skewness among attributes of the numeric type.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.

4 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
Define a new task