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.
Highlights
ROOT is an object-oriented framework that provides statistical methods, visualization and storage libraries for data analysis of high-energy physics (HEP) experiments, such as the Large Hadron Collider (LHC) in Geneva, Switzerland [1]
ROOT is a software framework for large-scale data analysis that provides basic and advanced statistical methods used by high-energy physics experiments
We present several recent developments in Toolkit for Multivariate Analysis (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
Summary
ROOT is an object-oriented framework that provides statistical methods, visualization and storage libraries for data analysis of high-energy physics (HEP) experiments, such as the Large Hadron Collider (LHC) in Geneva, Switzerland [1]. Originally designed for HEP applications, ROOT is widely used in other scientific fields outside of particle physics. The ROOT framework provides machine learning tools with the Toolkit for Multivariate Analysis (TMVA) [2],that contains many machine learning algorithms. TMVA provides a way to compare the performance of these algorithms on the same dataset, useful in choosing the optimal algorithm for a particular analysis task. TMVA has undergone a significant upgrade targeting greater flexibility, modular design, new features and interfaces. 2. New Algorithms and Features Several high-level algorithms and libraries have been added: deep neural networks, k-fold crossvalidation and hyper-parameter tuning. The new functionality and features of TMVA are described
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