dorsal/arxiv
View SchemaTMVA - Toolkit for Multivariate Data Analysis
| Authors | A. Hoecker, P. Speckmayer, J. Stelzer, J. Therhaag, E. von Toerne, H. Voss, M. Backes, T. Carli, O. Cohen, A. Christov, D. Dannheim, K. Danielowski, S. Henrot-Versille, M. Jachowski, K. Kraszewski, A. Krasznahorkay Jr., M. Kruk, Y. Mahalalel, R. Ospanov, X. Prudent, A. Robert, D. Schouten, F. Tegenfeldt, A. Voigt, K. Voss, M. Wolter, A. Zemla |
|---|---|
| Categories | |
| ArXiv ID | physics/0703039 |
| URL | https://arxiv.org/abs/physics/0703039 |
Abstract
In high-energy physics, with the search for ever smaller signals in ever larger data sets, it has become essential to extract a maximum of the available information from the data. Multivariate classification methods based on machine learning techniques have become a fundamental ingredient to most analyses. Also the multivariate classifiers themselves have significantly evolved in recent years. Statisticians have found new ways to tune and to combine classifiers to further gain in performance. Integrated into the analysis framework ROOT, TMVA is a toolkit which hosts a large variety of multivariate classification algorithms. Training, testing, performance evaluation and application of all available classifiers is carried out simultaneously via user-friendly interfaces. With version 4, TMVA has been extended to multivariate regression of a real-valued target vector. Regression is invoked through the same user interfaces as classification. TMVA 4 also features more flexible data handling allowing one to arbitrarily form combined MVA methods. A generalised boosting method is the first realisation benefiting from the new framework.
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"date_created": "2026-03-02T18:01:17.764000Z",
"date_modified": "2026-03-02T18:01:17.764000Z",
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"abstract": "In high-energy physics, with the search for ever smaller signals in ever\nlarger data sets, it has become essential to extract a maximum of the available\ninformation from the data. Multivariate classification methods based on machine\nlearning techniques have become a fundamental ingredient to most analyses. Also\nthe multivariate classifiers themselves have significantly evolved in recent\nyears. Statisticians have found new ways to tune and to combine classifiers to\nfurther gain in performance. Integrated into the analysis framework ROOT, TMVA\nis a toolkit which hosts a large variety of multivariate classification\nalgorithms. Training, testing, performance evaluation and application of all\navailable classifiers is carried out simultaneously via user-friendly\ninterfaces. With version 4, TMVA has been extended to multivariate regression\nof a real-valued target vector. Regression is invoked through the same user\ninterfaces as classification. TMVA 4 also features more flexible data handling\nallowing one to arbitrarily form combined MVA methods. A generalised boosting\nmethod is the first realisation benefiting from the new framework.",
"arxiv_id": "physics/0703039",
"authors": [
"A. Hoecker",
"P. Speckmayer",
"J. Stelzer",
"J. Therhaag",
"E. von Toerne",
"H. Voss",
"M. Backes",
"T. Carli",
"O. Cohen",
"A. Christov",
"D. Dannheim",
"K. Danielowski",
"S. Henrot-Versille",
"M. Jachowski",
"K. Kraszewski",
"A. Krasznahorkay Jr.",
"M. Kruk",
"Y. Mahalalel",
"R. Ospanov",
"X. Prudent",
"A. Robert",
"D. Schouten",
"F. Tegenfeldt",
"A. Voigt",
"K. Voss",
"M. Wolter",
"A. Zemla"
],
"categories": [
"physics.data-an"
],
"title": "TMVA - Toolkit for Multivariate Data Analysis",
"url": "https://arxiv.org/abs/physics/0703039"
},
"schema_id": "dorsal/arxiv",
"source": {
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"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
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