Instance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees

Brophy, Jonathan; Lowd, Daniel

doi:10.48550/arxiv.2205.11412

Cited by 2 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gradient boosting decision trees are among the most popular ML models, which often outperform NN as a point forecaster on benchmark tests with tabular data. Recently, probabilistic forecasts in gradient boosting decision trees have been studied in (Duan et al, 2020;Sprangers et al, 2021;Brophy and Lowd, 2022). While (Duan et al, 2020;Sprangers et al, 2021) require fitting a parametric distribution (such as Gaussian or Weibull) to the data, (Brophy and Lowd, 2022) allows to produce a more flexible, nonparametric distributional forecast.…”

Section: Related Workmentioning

confidence: 99%

Sample-based Uncertainty Quantification with a Single Deterministic Neural Network

Kanazawa¹,

Gupta²

2022

Preprint

View full text Add to dashboard Cite

Development of an accurate, flexible, and numerically efficient uncertainty quantification (UQ) method is one of fundamental challenges in machine learning. Previously, a UQ method called DISCO Nets has been proposed (Bouchacourt et al., 2016) that trains a neural network by minimizing the so-called energy score on training data. This method has shown superior performance on a hand pose estimation task in computer vision, but it remained unclear whether this method works as nicely for regression on tabular data, and how it competes with more recent advanced UQ methods such as NGBoost. In this paper, we propose an improved neural architecture of DISCO Nets that admits a more stable and smooth training. We benchmark this approach on miscellaneous real-world tabular datasets and confirm that it is competitive with or even superior to standard UQ baselines. We also provide a new elementary proof for the validity of using the energy score to learn predictive distributions. Further, we point out that DISCO Nets in its original form ignore epistemic uncertainty and only capture aleatoric uncertainty. We propose a simple fix to this problem.

show abstract

Section: Related Workmentioning

confidence: 99%

Sample-based Uncertainty Quantification with a Single Deterministic Neural Network

Kanazawa¹,

Gupta²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Examples of parametric methods include Natural Gradient Boosting for Probabilistic Prediction (NGBoost) [17], XGBoostLSS and LightGBMLSS, which model all moments of a parametric distribution: mean, location, scale, and shape (LSS) [18,19], Catboost with Uncertainty (CBU), Probabilistic Gradient Boosting Machines (PGBM) [20], and Instance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees (IBUG) [21].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Machine Learning Methods for Fractional Brownian Motion Time Series Forecasting

Kirichenko,

Lavrynenko

2023

Fractal Fract

View full text Add to dashboard Cite

This paper explores the capabilities of machine learning for the probabilistic forecasting of fractional Brownian motion (fBm). The focus is on predicting the probability of the value of an fBm time series exceeding a certain threshold after a specific number of time steps, given only the knowledge of its Hurst exponent. The study aims to determine if the self-similarity property is preserved in a forecasting time series and which machine learning algorithms are the most effective. Two types of forecasting methods are investigated: methods with a predefined distribution shape and those without. The results show that the self-similar properties of the fBm time series can be reliably reproduced in the continuations of the time series predicted by machine learning methods. The study also provides an experimental comparison of various probabilistic forecasting methods and their potential applications in the analysis and modeling of fractal time series.

show abstract

Instance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees

Cited by 2 publications

References 26 publications

Sample-based Uncertainty Quantification with a Single Deterministic Neural Network

Sample-based Uncertainty Quantification with a Single Deterministic Neural Network

Probabilistic Machine Learning Methods for Fractional Brownian Motion Time Series Forecasting

Contact Info

Product

Resources

About