Data
Higgs

Higgs

active ARFF Publicly available Visibility: public Uploaded 11-12-2020 by Marcos de Paula Bueno
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This is a smaller version of the original dataset, containing 1M rows. 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

target (target)nominal2 unique values
0 missing
lepton_pTnumeric19815 unique values
0 missing
lepton_etanumeric5001 unique values
0 missing
lepton_phinumeric6284 unique values
0 missing
missing_energy_magnitudenumeric616259 unique values
0 missing
missing_energy_phinumeric637068 unique values
0 missing
jet_1_ptnumeric34131 unique values
0 missing
jet_1_etanumeric5999 unique values
0 missing
jet_1_phinumeric6284 unique values
0 missing
jet_1_b-tagnumeric3 unique values
0 missing
jet_2_ptnumeric26960 unique values
0 missing
jet_2_etanumeric5999 unique values
0 missing
jet_2_phinumeric6284 unique values
0 missing
jet_2_b-tagnumeric3 unique values
0 missing
jet_3_ptnumeric18733 unique values
0 missing
jet_3_etanumeric5999 unique values
0 missing
jet_3_phinumeric6284 unique values
0 missing
jet_3_b-tagnumeric3 unique values
0 missing
jet_4_ptnumeric14035 unique values
0 missing
jet_4_etanumeric5999 unique values
0 missing
jet_4_phinumeric6284 unique values
0 missing
jet_4_b-tagnumeric3 unique values
0 missing
m_jjnumeric502300 unique values
0 missing
m_jjjnumeric213126 unique values
0 missing
m_lvnumeric185039 unique values
0 missing
m_jlvnumeric260395 unique values
0 missing
m_bbnumeric455299 unique values
0 missing
m_wbbnumeric352577 unique values
0 missing
m_wwbbnumeric406261 unique values
0 missing

19 properties

1000000
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).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
28
Number of numeric attributes.
1
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
96.55
Percentage of numeric attributes.
52.99
Percentage of instances belonging to the most frequent class.
3.45
Percentage of nominal attributes.
529920
Number of instances belonging to the most frequent class.
47.01
Percentage of instances belonging to the least frequent class.
470080
Number of instances belonging to the least frequent class.
1
Number of binary attributes.
3.45
Percentage of binary attributes.
0
Percentage of instances having missing values.
1
Average class difference between consecutive instances.
0
Percentage of missing values.

1 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: area_under_roc_curve - target_feature: target
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