Related papers: Studies of Boosted Decision Trees for MiniBooNE Pa…
Global optimization of decision trees is a long-standing challenge in combinatorial optimization, yet such models play an important role in interpretable machine learning. Although the problem has been investigated for several decades, only…
The NuMI facility at Fermilab will provide an extremely intense beam of neutrinos for the MINOS neutrino-oscillation experiment. The spacious and fully-outfitted MINOS near detector hall will be the ideal venue for a high-statistics,…
This paper describes RESOLVE, a system that uses decision trees to learn how to classify coreferent phrases in the domain of business joint ventures. An experiment is presented in which the performance of RESOLVE is compared to the…
The observation of neutrino oscillations is clear evidence for physics beyond the standard model. To make precise measurements of this phenomenon, neutrino oscillation experiments, including MiniBooNE, require an accurate description of…
MicroBooNE (the Micro Booster Neutrino Experiment) is a short-baseline neutrino experiment based on the technology of a liquid-argon time-projection chamber (LArTPC), and has recently completed its first year of data-taking in the Fermilab…
This paper proposes a new mixed-integer programming (MIP) formulation to optimize split rule selection in the decision tree induction process, and develops an efficient search algorithm that is able to solve practical instances of the MIP…
We present a simple introduction to the decision tree algorithm using some examples from nuclear physics. We show how to improve the accuracy of the classical liquid drop nuclear mass model by performing Feature Engineering with a decision…
The Minos+ experiment is an extension of the Minos experiment at a higher energy and more intense neutrino beam, with the data collection having begun in the fall of 2013. The neutrino beam is provided by the Neutrinos from the Main…
Nonlinear metrics, such as the F1-score, Matthews correlation coefficient, and Fowlkes-Mallows index, are often used to evaluate the performance of machine learning models, in particular, when facing imbalanced datasets that contain more…
Gradient Boosted Decision Trees (GBDTs) are widely used for building ranking and relevance models in search and recommendation. Considerations such as latency and interpretability dictate the use of as few features as possible to train…
Decision trees are widely used for interpretable machine learning due to their clearly structured reasoning process. However, this structure belies a challenge we refer to as predictive equivalence: a given tree's decision boundary can be…
Natural gradient has been recently introduced to the field of boosting to enable the generic probabilistic predication capability. Natural gradient boosting shows promising performance improvements on small datasets due to better training…
We give a quantum algorithm for evaluating a class of boolean formulas (such as NAND trees and 3-majority trees) on a restricted set of inputs. Due to the structure of the allowed inputs, our algorithm can evaluate a depth $n$ tree using…
Retrieving relevant targets from an extremely large target set under computational limits is a common challenge for information retrieval and recommendation systems. Tree models, which formulate targets as leaves of a tree with trainable…
Despite the success of deep learning in computer vision and natural language processing, Gradient Boosted Decision Tree (GBDT) is yet one of the most powerful tools for applications with tabular data such as e-commerce and FinTech. However,…
In fast ramping synchrotrons like the Fermilab Booster the conventional methods of betatron tune evaluation from the turn-by-turn data may not work due to rapid changes of the tunes (sometimes in a course of a few dozens of turns) and a…
Over the last two decades, several experiments have reported anomalous results that could be hinting at the exciting possibility of sterile neutrino states in the $eV^{2}$ mass scale. Liquid Argon Time Projection Chambers (LArTPCs) are a…
When data have a hierarchical structure, such as students nested within classrooms, ignoring dependencies between observations can compromise the validity of imputation procedures. Standard tree-based imputation methods implicitly assume…
Precise, accurate neutrino flux predictions for neutrino beam experiments are crucial for physics results. Flux predictions for the Booster Neutrino Beam at Fermilab were first published in 2009 by the MiniBooNE collaboration. It is no…
Recently, MiniBooNE observed an electromagnetic excess at low energy. What is the nature of this excess? What about the nature of the low-energy excess at LSND 20 years ago? The MicroBooNE detector will see neutrinos from the same Booster…