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hls4ml_lhc_jets_hlf

hls4ml_lhc_jets_hlf

active ARFF Attribution (CC BY) Visibility: public Uploaded 02-06-2020 by Sioni Summers
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  • Earth Science Machine Learning
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Identify jets of particles from the LHC, created for the study of ultra low latency inference with hls4ml. Use 16 high level features to identify the 5 jet classes: quark (q), gluon (g), W boson (w), Z boson (z), or top quark (t). The hls4ml paper: https://iopscience.iop.org/article/10.1088/1748-0221/13/07/P07027 The dataset DOI: https://doi.org/10.5281/zenodo.3602260

17 features

class (target)nominal5 unique values
0 missing
zlogznumeric791560 unique values
0 missing
c1_b0_mmdtnumeric737519 unique values
0 missing
c1_b1_mmdtnumeric816552 unique values
0 missing
c1_b2_mmdtnumeric820025 unique values
0 missing
c2_b1_mmdtnumeric821168 unique values
0 missing
c2_b2_mmdtnumeric825794 unique values
0 missing
d2_b1_mmdtnumeric806613 unique values
0 missing
d2_b2_mmdtnumeric818805 unique values
0 missing
d2_a1_b1_mmdtnumeric806613 unique values
0 missing
d2_a1_b2_mmdtnumeric809671 unique values
0 missing
m2_b1_mmdtnumeric811360 unique values
0 missing
m2_b2_mmdtnumeric818189 unique values
0 missing
n2_b1_mmdtnumeric801495 unique values
0 missing
n2_b2_mmdtnumeric816406 unique values
0 missing
mass_mmdtnumeric804072 unique values
0 missing
multiplicitynumeric188 unique values
0 missing
Show all 17 features

19 properties

NumberOfInstances
830000
Number of instances (rows) of the dataset.
NumberOfFeatures
17
Number of attributes (columns) of the dataset.
NumberOfClasses
5
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
16
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
Dimensionality
0
Number of attributes divided by the number of instances.
PercentageOfNumericFeatures
94.12
Percentage of numeric attributes.
MajorityClassPercentage
20.22
Percentage of instances belonging to the most frequent class.
PercentageOfSymbolicFeatures
5.88
Percentage of nominal attributes.
MajorityClassSize
167851
Number of instances belonging to the most frequent class.
MinorityClassPercentage
19.41
Percentage of instances belonging to the least frequent class.
MinorityClassSize
161066
Number of instances belonging to the least frequent class.
NumberOfBinaryFeatures
0
Number of binary attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
AutoCorrelation
0.2
Average class difference between consecutive instances.
PercentageOfMissingValues
0
Percentage of missing values.
Show all 19 properties

8 tasks

Learning Curve on hls4ml_lhc_jets_hlf
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on hls4ml_lhc_jets_hlf
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on hls4ml_lhc_jets_hlf
0 runs - estimation_procedure: 50 times Clustering
Clustering on hls4ml_lhc_jets_hlf
0 runs - estimation_procedure: 50 times Clustering
Clustering on hls4ml_lhc_jets_hlf
0 runs - estimation_procedure: 50 times Clustering
Clustering on hls4ml_lhc_jets_hlf
0 runs - estimation_procedure: 50 times Clustering
Clustering on hls4ml_lhc_jets_hlf
0 runs - estimation_procedure: 50 times Clustering
Clustering on hls4ml_lhc_jets_hlf
0 runs - estimation_procedure: 50 times Clustering
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