Abstract
ROOT is a software framework for large-scale data analysis that provides basic and advanced statistical methods used by high-energy physics experiments. It includes machine learning tools from the ROOT-integrated Toolkit for Multivariate Analysis (TMVA). We present several recent developments in TMVA, including a new modular design, new algorithms for pre-processing, cross-validation, hyperparameter-tuning, deep-learning and interfaces to other machine-learning software packages. TMVA is additionally integrated with Jupyter, making it accessible with a browser.
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CITATION STYLE
Bagoly, A., Bevan, A., Carnes, A., Gleyzer, S. V., Moneta, L., Moudgil, A., … Zapata, O. (2017). Machine Learning Developments in ROOT. In Journal of Physics: Conference Series (Vol. 898). Institute of Physics. https://doi.org/10.1088/1742-6596/898/7/072046
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