Greedy function approximation: A gradient boosting machine.

2001/10/01 by Jerome H. Friedman · 29,600 citations
Computer Science · Mathematics · Physics and Astronomy · #Applied mathematics #Artificial intelligence #Artificial neural network #Boosting (machine learning) #Computer science #Gradient boosting #Gradient descent #Logistic regression #Machine Learning and Algorithms #Mathematical optimization #Mathematics #Minification #Model Reduction and Neural Networks #Neural Networks and Applications #Random forest #Regression #Statistics

paper · pdf · doi:10.1214/aos/1013203451

published in The Annals of Statistics 29(5) (Institute of Mathematical Statistics)

openalex publication_date 2001/10/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06

Abstract

Function estimation/approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest-descent minimization. A general gradient descent “boosting” paradigm is developed for additive expansions based on any fitting criterion.Specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification. Special enhancements are derived for the particular case where the individual additive components are regression trees, and tools for interpreting such “TreeBoost” models are presented. Gradient boosting of regression trees produces competitive, highly robust, interpretable procedures for both regression and classification, especially appropriate for mining less than clean data. Connections between this approach and the boosting methods of Freund and Shapire and Friedman, Hastie and Tibshirani are discussed.

Citations

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• De Novo Design of Inhibitors of DNA Methyltransferase 1: A Critical Comparison of Ligand- and Structure-Based Approaches
• Machine learning-guided discovery and design of non-hemolytic peptides
• Advances in Boosting (Invited Talk)
• Oblique Decision Trees from Derivatives of ReLU Networks
• BenSParX: A Robust Explainable Machine Learning Framework for Parkinson's Disease Detection from Bengali Conversational Speech
• A Case for Library-Level k-Means Binning in Histogram Gradient-Boosted Trees
• Is rotation forest the best classifier for problems with continuous features?
• Technical Report: Partial Dependence through Stratification
• LAMP: Extracting Locally Linear Decision Surfaces from LLM World Models
• Selective Cascade of Residual ExtraTrees
• Explainable Machine Learning for Oxygen Diffusion in Perovskites and Pyrochlores
• Most General Explanations of Tree Ensembles (Extended Version)
• Automatic identification of saltating tracks driven by strong wind in high-speed video using multiple statistical quantities of instant particle velocity
• Testing the socioeconomic and environmental determinants of better child-health outcomes in Africa: a cross-sectional study among nations
• Detection of Cliff Top Erosion Drivers through Machine Learning Algorithms between Portonovo and Trave Cliffs (Ancona, Italy)
• Simulation-Based Plate-Reverb Parameter Estimation from a Single Impulse Response
• Context-aware Reranking with Utility Maximization for Recommendation
• Covariate-moderated Empirical Bayes Matrix Factorization
• Individual Explanations in Machine Learning Models: A Survey for Practitioners
• Towards Zero-resource Cross-lingual Entity Linking
• Quantum Computing and AI: Perspectives on Advanced Automation in Science and Engineering
• GBM Returns the Best Prediction Performance among Regression Approaches: A Case Study of Stack Overflow Code Quality
• Characterizing Implicit Bias in Terms of Optimization Geometry
• Smart Prediction of the Complaint Hotspot Problem in Mobile Network
• Adaptive Covariate Acquisition for Minimizing Total Cost of Classification
• Probing the refined performance of the Categorical-Boosting algorithm to the Hartree-Fock-Bogoliubov mass model with different Skyrme forces
• SStaGCN: Simplified stacking based graph convolutional networks
• A General Framework for Fast Stagewise Algorithms
• Structure-aware machine learning strategies for antimicrobial peptide discovery
• Identifying Politically Connected Firms: A Machine Learning Approach*
• Estimating the distribution of Oryzomys palustris, a potential key host in expanding rickettsial tick‐borne disease risk
• What’s eating public transit in the United States? Reasons for declining transit ridership in the 2010s
• Interpretable Machine Learning for Cross‐Cohort Prediction of Motor Fluctuations in Parkinson's Disease
• XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction
• Evaluating the sample size requirements of tree-based ensemble machine learning techniques for clinical risk prediction
• Wikipedia Edit Number Prediction based on Temporal Dynamics Only
• GPU-acceleration for Large-scale Tree Boosting
• Random forests for global sensitivity analysis: A selective review
• A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML
• Environmental and spatial correlates of hydrologic alteration in a large Mediterranean river catchment
• Multiple Human Tracking using Multi-Cues including Primitive Action Features
• RoNGBa: A Robustly Optimized Natural Gradient Boosting Training Approach with Leaf Number Clipping
• Identification of relevant diffusion MRI metrics impacting cognitive functions using a novel feature selection method
• Dive into Decision Trees and Forests: A Theoretical Demonstration
• Effects of browsing by white-tailed deer on tree regeneration vary by ontogeny and palatability in forests of the northeastern USA
• AI and Generative AI Transforming Disaster Management: A Survey of Damage Assessment and Response Techniques
• Solving Nonlinear PDEs with Sparse Radial Basis Function Networks
• Towards Confident Machine Reading Comprehension
• Learning Penalty for Optimal Partitioning via Automatic Feature Extraction
• Peer Relationships Are a Direct Cause of the Adolescent Mental Health Crisis: Interpretable Machine Learning Analysis of 2 Large Cohort Studies
• Unwrapping The Black Box of Deep ReLU Networks: Interpretability, Diagnostics, and Simplification
• Cyclic Boosting -- an explainable supervised machine learning algorithm
• Finding Important Genes from High-Dimensional Data: An Appraisal of Statistical Tests and Machine-Learning Approaches
• Predicting Diabetic Macular Edema Treatment Responses Using OCT: Dataset and Methods of APTOS Competition
• When are Deep Networks really better than Decision Forests at small sample sizes, and how?
• Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction
• A LightGBM-Incorporated Absorbing Boundary Conditions for the Wave-Equation-Based Meshless Method
• Light Gradient Boosting Machine as a Regression Method for Quantitative Structure-Activity Relationships
• Missing links prediction: comparing machine learning with physics-rooted approaches
• Multilevel Calibration Weighting for Survey Data
• Deep-MAPS: Machine Learning based Mobile Air Pollution Sensing
• Nonlinear and interaction effects of land use and motorcycles/E-bikes on car ownership
• A comparative analysis of gradient boosting algorithms
• Various Approaches to Aspect-based Sentiment Analysis
• What determines trophic niche breadth? A global analysis of freshwater fishes using isospaces
• Multi-Objective Automatic Machine Learning with AutoxgboostMC
• Benchmarking Support Vector Machines
• HDI-Forest: Highest Density Interval Regression Forest
• DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values
• Persistence B-Spline Grids: Stable Vector Representation of Persistence Diagrams Based on Data Fitting
• Rectified Decision Trees: Towards Interpretability, Compression and Empirical Soundness
• Asymmetric Penalties Underlie Proper Loss Functions in Probabilistic Forecasting
• Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling
• New multicategory boosting algorithms based on multicategory Fisher-consistent losses
• Testing and Emulating Modified Gravity on Cosmological Scales
• Modeling Heterogeneity in Mode-Switching Behavior Under a Mobility-on-Demand Transit System: An Interpretable Machine Learning Approach
• Boosting With theL2Loss
• Boosting Algorithms: Regularization, Prediction and Model Fitting
• Cerebrospinal fluid markers reveal intrathecal inflammation in progressive multiple sclerosis
• Statistical Applications to Downscale GRACE-Derived Terrestrial Water Storage Data and to Fill Temporal Gaps
• Predictive learning via rule ensembles
• MOAI: A methodology for evaluating the impact of indoor airflow in the transmission of COVID-19
• Predicting and Understanding Law-Making with Word Vectors and an Ensemble Model
• A Taxi Order Dispatch Model based On Combinatorial Optimization
• Transforming Graph Representations for Statistical Relational Learning
• Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle
• AOSO-LogitBoost: Adaptive One-Vs-One LogitBoost for Multi-Class Problem
• A Computational Evaluation of Musical Pattern Discovery Algorithms
• Digital Urban Sensing: A Multi-layered Approach
• Analysis of Regression Tree Fitting Algorithms in Learning to Rank
• Using GAN-based models to sentimental analysis on imbalanced datasets in education domain
• Machine Learning Meets Transparency in Osteoporosis Risk Assessment: A Comparative Study of ML and Explainability Analysis
• Statistical Learning for Heterogeneous Treatment Effects: Pretraining, Prognosis, and Prediction
• Hierarchical Additive Modeling of Nonlinear Association with Spatial Correlations-An Application to Relate Alcohol Outlet Density and Neighborhood Assault Rates
• SWIR-based mineral assemblages for interpretable and transferable gold grade prediction: insights from the Zhangjiapingzi gold deposit
• Operational fault diagnosis of autonomous underwater vehicles via a hybrid descriptor-temporal stacking framework
• Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus
• Evaluation of soccer team defense based on prediction models of ball recovery and being attacked: A pilot study
• Topological Properties and Temporal Dynamics of Place Networks in Urban Environments
• Spatial econometrics to estimate traffic reduction by transforming office space into housing and other land uses: The case for Barcelona
• Machine Learning for analysis of Multiple Sclerosis cross-tissue bulk and single-cell transcriptomics data
• Jointly learning relevant subgraph patterns and nonlinear models of their indicators
• Rule Covering for Interpretation and Boosting
• Parallel Bayesian Additive Regression Trees
• The Shooting Regressor; Randomized Gradient-Based Ensembles
• Robust LogitBoost and Adaptive Base Class (ABC) LogitBoost
• LoBoost: Fast Model-Native Local Conformal Prediction for Gradient-Boosted Trees
• Global Patterns of Leaf Litter C:N:P Stoichiometry Under Current and Future Climate Scenarios
• Mapping global environmental suitability for Zika virus
• Boosting in the Presence of Outliers: Adaptive Classification With Nonconvex Loss Functions
• Multi-variable LSTM neural network for autoregressive exogenous model
• Neural information retrieval: at the end of the early years
• Distilling Black-Box Travel Mode Choice Model for Behavioral Interpretation
• Feature-Based Magnitude Estimates for Small Earthquakes in the Yellowstone Region
• Bilateral Differentially Private Vertical Federated Boosted Decision Trees
• What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models
• Bridging the Generalisation Gap: Synthetic Data Generation for Multi-Site Clinical Model Validation
• Global Warming’s “Six Americas Short Survey”: Audience Segmentation of Climate Change Views Using a Four Question Instrument
• Machine learning via artificial neural networks coupled with density functional theory and experiments for thermodynamic optimization of high-entropy alloys for hydrogen storage at room temperature
• Search for a new resonance decaying to a Higgs boson and a scalar boson in events with two b jets and two Z bosons in proton-proton collisions at √(s) = 13.6 TeV
• Algorithmic Monitoring: Measuring Market Stress with Machine Learning
• NFISiS: New Perspectives on Fuzzy Inference Systems for Renewable Energy Forecasting
• Bayesian Nonlinear Models for Repeated Measurement Data: An Overview, Implementation, and Applications
• Kernel Matching Pursuit
• What is Machine Learning? A Primer for the Epidemiologist
• The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus
• Machine learning techniques to select Be star candidates
• Learning to fuse: dynamic integration of multi-source data for accurate battery lifespan prediction
• Quantitative 3D imaging of mouse and human intrahepatic bile ducts in homeostasis and liver injury
• Prediction of gully erosion susceptibility through the lens of the SHapley Additive exPlanations (SHAP) method using a stacking ensemble model
• Decomposing Network Influence: Social Influence Regression
• Reliability-Aware ETF Tail-Risk Monitoring
• Understanding web browsing behaviors through Weibull analysis of dwell time
• Assessing the potential impact of invasive ring-necked parakeets Psittacula krameri on native nuthatches Sitta europeae in Belgium
• An Imbalanced Dataset with Multiple Feature Representations for Studying Quality Control of Next-Generation Sequencing
• Marking-Aware Sequential VaR Recalibration for Standardized Option Books
• Why is Regularization Underused? An Empirical Study on Trust and Adoption of Statistical Methods
• Generalized Inverse Planning: Learning Lifted non-Markovian Utility for Generalizable Task Representation
• Ensemble deep learning: A review
• Return on investment on artificial intelligence: The case of bank capital requirement
• Data-Driven Modeling Reveals the Impact of Stay-at-Home Orders on Human Mobility during the COVID-19 Pandemic in the U.S
• FOUND-AF: Benchmarking ECG Foundation Models for Atrial Fibrillation Detection
• Machine learning reveals how personalized climate communication can both succeed and backfire
• Classifying sleep states using persistent homology and Markov chain: a Pilot Study
• Selective Machine Learning of the Average Treatment Effect with an Invalid Instrumental Variable
• Match-Tensor: a Deep Relevance Model for Search
• Pitfalls in machine learning interpretability: Manipulating partial dependence plots to hide discrimination
• Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced Datasets
• Identifying the environmental and anthropogenic causes, distribution, and intensity of human rhesus macaque conflict in Nepal
• Identifying and prioritizing potential human-infecting viruses from their genome sequences
• Data-Driven Surrogate Modeling Techniques to Predict the Effective Contact Area of Rough Surface Contact Problems
• Sewer pipes’ lifespan prediction: can we modify the data to make the machine learning algorithms fit the purpose?
• Causal rule ensemble approach for multi-arm data
• Common Functional Decompositions Can Mis-attribute Differences in Outcomes Between Populations
• Machine learning-driven capacity design and embodied carbon reduction optimization in composite reduced web section (RWS) connections
• Systemic Flakiness: An Empirical Analysis of Co-Occurring Flaky Test Failures
• Performance of electron reconstruction and selection with the CMS detector in proton-proton collisions at √s= 8 TeV
• Short-term load forecasting under EU-AI Act Requirements in Safety-Critical Environments: Results from a 41-day live challenge on the aggregated German transmission-grid load
• GeoReward: Mitigating Contextual Variable Overestimation in Vision-Language Models for Cross-Market Preference Prediction
• SparseDitto: Customizing GPU Kernels for Different Sparsity Patterns with LLM-Based Agentic System
• Predicting Water Temperature Dynamics of Unmonitored Lakes With Meta‐Transfer Learning
• Approximation Trees: Statistical Stability in Model Distillation
• Scaling up Memory-Efficient Formal Verification Tools for Tree Ensembles
• FiNCAT: Financial Numeral Claim Analysis Tool
• Personalized machine learning for robot perception of affect and engagement in autism therapy
• Chromatic Learning for Sparse Datasets
• Convex Risk Minimization and Conditional Probability Estimation
• A New Deterministic Technique for Symbolic Regression
• Are NBA Players’ Salaries in Accordance with Their Performance on Court?
• Extending Statistical Boosting
• A Manually Annotated Chinese Corpus for Non-task-oriented Dialogue Systems
• R^*: A robust MCMC convergence diagnostic with uncertainty using decision tree classifiers
• Open Source Software Lifecycle Classification: Developing Wrangling Techniques for Complex Sociotechnical Systems
• Analytic Continued Fractions for Regression: A Memetic Algorithm Approach
• Machine learning in critical heat flux studies in nuclear systems: A detailed review
• BART: Bayesian additive regression trees
• One Explanation Does Not Fit All
• Comparative machine learning and deep learning frameworks for robust carcinogenicity prediction and activity cliff analysis
• Enhancing Certifiable Robustness via a Deep Model Ensemble
• Lightweight Latent Verifiers for Efficient Meta-Generation Strategies
• Bayesian neural networks for detecting epistasis in genetic association studies
• Variance Minimization in the Wasserstein Space for Invariant Causal Prediction
• Consistent Causal Inference of Group Effects in Non-Targeted Trials with Finitely Many Effect Levels
• Exploiting Categorical Structure Using Tree-Based Methods
• Social Catalysts: Characterizing People Who Spark Conversations Among Others
• Impact of Iridium Crucible Aging on Cz‐YAG Crystal Quality and Process Economy: A Data‐Driven Study
• Boosting KNNClassifier Performance with Opposition-Based Data Transformation
• Clustering Wi-Fi fingerprints for indoor–outdoor detection
• Heterogeneous networks in drug-target interaction prediction
• Ensemble Learning for Blending Gridded Satellite and Gauge-Measured Precipitation Data
• A review of machine learning applications in wildfire science and management
• Predicting the global far-infrared SED of galaxies via machine learning techniques
• Modeling Binary Time Series Using Gaussian Processes with Application to Predicting Sleep States
• Predicting Movie Genres Based on Plot Summaries
• Invariance-embedded Machine Learning Sub-grid-scale Stress Models for Meso-scale Hurricane Boundary Layer Flow Simulation I: Model Development and a priori Studies
• Translocatome: a novel resource for the analysis of protein translocation between cellular organelles
• Permeability prediction of porous media using a combination of computational fluid dynamics and hybrid machine learning methods
• Prediction of Coronary Heart Disease Using Routine Blood Tests
• Dynamic Regularized CBDT: Variance-Calibrated Causal Boosting for Interpretable Heterogeneous Treatment Effects
• Modelling the COVID-19 virus evolution with Incremental Machine Learning
• DeePKS: A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory
• Scared into Action: How Partisanship and Fear are Associated with Reactions to Public Health Directives
• Observation of the decay Λb0→Λc+pp‾π−
• Urban Anomaly Analytics: Description, Detection, and Prediction
• Enhancing discrete choice models with representation learning
• Contrast trees and distribution boosting
• Infusing domain knowledge in AI-based "black box" models for better explainability with application in bankruptcy prediction
• Predicting acute kidney injury at hospital re-entry using high-dimensional electronic health record data
• Estimation of impact parameter and transverse spherocity in heavy-ion collisions at the LHC energies using machine learning
• Statistical classification techniques for photometric supernova typing
• Is MOOC Learning Different for Dropouts? A Visually-Driven, Multi-granularity Explanatory ML Approach
• Predictive Analytics Using Social Big Data and Machine Learning
• MP-Boost: Minipatch Boosting via Adaptive Feature and Observation Sampling
• Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes
• Personalized Nutrition by Prediction of Glycemic Responses
• Ensemble Sales Forecasting Study in Semiconductor Industry
• The ETS challenges: a machine learning approach to the evaluation of\n simulated financial time series for improving generation processes
• Reducing CO2emissions by targeting the world’s hyper-polluting power plants*
• Anomaly detection for machine learning redshifts applied to SDSS galaxies
• Alert Classification for the ALeRCE Broker System: The Light Curve Classifier
• FIST: A Feature-Importance Sampling and Tree-Based Method for Automatic Design Flow Parameter Tuning
• Flood detection and mapping through multi-resolution sensor fusion: integrating UAV optical imagery and satellite SAR data
• The unified maximum a posteriori (MAP) framework for neuronal system identification
• Euclid preparation. Estimating galaxy physical properties using CatBoost chained regressors with attention
• The effects of data quality on machine learning performance on tabular data
• Machine Learning Reveals Composition Dependent Thermal Stability in Halide Perovskites
• Representation Learning for Tabular Data: A Comprehensive Survey
• Comparative Evaluation of Radiomics and Deep Learning Models for Disease Detection in Chest Radiography
• Machine learning in flight parameter-based structural load prediction: A review and framework proposal
• Towards Human-Centered Early Prediction Models for Academic Performance in Real-World Contexts
• Heavy neutrino mixing prospects at hadron colliders: a machine learning study
• M²FGB: A Min-Max Gradient Boosting Framework for Subgroup Fairness
• Can Moran Eigenvectors Improve Machine Learning of Spatial Data? Insights from Synthetic Data Validation
• Patterns of West Nile virus vector co-occurrence and spatial overlap with human cases across Europe
• Emotion-focused therapy for incarcerated offenders of intimate partner violence: A 3-year outcome using a new whole-sample matching method
• Explaining Anomalies Detected by Autoencoders Using SHAP
• Fast-Powerformer: A Memory-Efficient Transformer for Accurate Mid-Term Wind Power Forecasting
• MiMIC: Multi-Modal Indian Earnings Calls Dataset to Predict Stock Prices
• Generalised Boosted Forests
• Predicting the Lifespan of Industrial Printheads with Survival Analysis
• GPT Carry-On: Training Foundation Model for Customization Could Be Simple, Scalable and Affordable
• Network On Network for Tabular Data Classification in Real-world Applications
• Identification and estimation of causal peer effects using instrumental variables
• Actuarial Learning for Pension Fund Mortality Forecasting