LightGBM: A Highly Efficient Gradient Boosting Decision Tree

By Guolin Ke, Qi Meng, Thomas Finley, Weidong Ma, Qiwei Ye, Tie-Yan Liu. 2017

15,550 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Optimization & Training

Citations in the corpus

Multi-Fidelity Automatic Hyper-Parameter Tuning via Transfer Series Expansion (2019, methodology): “From experiments on Light-GBM (Ke et al. 2017) hyper-parameter tuning tasks, TSE can effectively ﬁx the biases with only few high-ﬁdelity observations.”
How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning (2021, methodology): “Therefore, we compared the proposed methods with baselines with slight modiﬁcations: we resized the ﬁne resolution (20 × 20) unit-cell to the original size (10 × 10) and applied two algorithms (CNN or LightGBM) for rejection sampling.”
Revisiting Deep Learning Models for Tabular Data (2021, background): “In these problems, data points are represented as vectors of heterogeneous features, which is typical for industrial applications and ML competitions, where neural networks have a strong non-deep competitor in the form of GBDT (Chen and Guestrin, 2016; Ke et al., 2017; Prokhorenkova et al., 2018).”
Forecasting gold price with the XGBoost algorithm and SHAP interaction values (2021, neutral): “LightGBM is a novel gradient boosting framework proposed by Ke et al. (2017).”
UTMOS: UTokyo-SaruLab System for VoiceMOS Challenge 2022 (2022, neutral): “The simple regression methods for the weak and meta learners were two linear regressions (ridge regression and linear support vector regression (SVR)), two tree-based models (random forests and LightGBM [27]), and two kernel methods (kernel SVR and Gaussian process regression).”
Feature selection strategies: a comparative analysis of SHAP-value and importance-based methods (2024, result): “LightGBM [11] was not included in our choices due to its poor performance, as indicated by our preliminary results in comparison to other learners.”
CatBoost for big data: an interdisciplinary review (2020, background): “In their study the authors report using CatBoost, XGBoost, and LightGBM.”
Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning (2020, methodology): “As a result, we used LightGBM [30], an implementation of gradient boosted decision trees, to create the underlying model to be explained using the SHAP Python package.”
EA-LSTM: Evolutionary Attention-based LSTM for Time Series Prediction (2018, methodology): “…1996] based on kernel method and artiﬁcial neural networks (ANN) [Davoian and Lippe, 2007] with the strong nonlinear function approximation ability and tree-based ensemble learning method, for instance, gradient boosting regression or decision tree (GBRT, GBDT) [Li and Bai, 2016; Ke et al. , 2017].”
Early detection of type 2 diabetes mellitus using machine learning-based prediction models (2020, methodology): “Models improve the accuracy by fitting negative gradients, named also as residual errors 23 , which in regression symbolises a difference between expected and predicted value.”
A comparative analysis of gradient boosting algorithms (2019, neutral): “2018) and in training speed for the latter (Ke et al. 2017).”
EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models (2018, methodology): “From the vectorized features, we trained a gradient-boosed decision tree (GBDT) model using LightGBM with default parameters (100 trees, 31 leaves per tree), resulting in fewer than 10K tunable parameters [14].”