CatBoost: gradient boosting with categorical features support

Abstract

In this paper we present CatBoost, a new open-sourced gradient boosting library that successfully handles categorical features and outperforms existing publicly available implementations of gradient boosting in terms of quality on a set of popular publicly available datasets. The library has a GPU implementation of learning algorithm and a CPU implementation of scoring algorithm, which are significantly faster than other gradient boosting libraries on ensembles of similar sizes.

References in corpus (2)

• GPU-acceleration for Large-scale Tree Boosting
• Enhancing LambdaMART Using Oblivious Trees

Cited by in corpus (58)

• AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
• SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training
• Pay Attention to Evolution: Time Series Forecasting with Deep Graph-Evolution Learning
• The Nature of High ${[\mathrm{OIII}]}_{88\mu \mathrm{m}}$/${[\mathrm{CII}]}_{158\mu \mathrm{m}}$ Galaxies in the Epoch of Reionization: Low Carbon Abundance and a Top-Heavy IMF?
• Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring
• A Machine Learning Analysis of COVID-19 Mental Health Data
• A Convolutional Neural Network Approach to the Classification of Engineering Models
• Hateminers : Detecting Hate speech against Women
• Explainable expected goal models for performance analysis in football analytics
• Artificial Intelligence-Based Analytics for Impacts of COVID-19 and Online Learning on College Students' Mental Health
• DFSeer: A Visual Analytics Approach to Facilitate Model Selection for Demand Forecasting
• Housing Market Prediction Problem using Different Machine Learning Algorithms: A Case Study
• SANGRIA: Stacked Autoencoder Neural Networks with Gradient Boosting for Indoor Localization
• Multi-Objective Automatic Machine Learning with AutoxgboostMC
• SeqNAS: Neural Architecture Search for Event Sequence Classification
• ProtoAttend: Attention-Based Prototypical Learning
• Fashion Retail: Forecasting Demand for New Items
• Hyperboost: Hyperparameter Optimization by Gradient Boosting surrogate models
• Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL
• CatBoostLSS -- An extension of CatBoost to probabilistic forecasting
• Performance Modeling of Data Storage Systems using Generative Models
• PSD2 Explainable AI Model for Credit Scoring
• PaloBoost: An Overfitting-robust TreeBoost with Out-of-Bag Sample Regularization Techniques
• LightAutoML: AutoML Solution for a Large Financial Services Ecosystem
• Novel Techniques to Assess Predictive Systems and Reduce Their Alarm Burden
• MP-Boost: Minipatch Boosting via Adaptive Feature and Observation Sampling
• On marginal feature attributions of tree-based models
• Predicting Research Trends in Artificial Intelligence with Gradient Boosting Decision Trees and Time-aware Graph Neural Networks
• Deep Learning-Based Position Detection for Hydraulic Cylinders Using Scattering Parameters
• On Gradient Boosted Decision Trees and Neural Rankers: A Case-Study on Short-Video Recommendations at ShareChat
• Deep differentiable forest with sparse attention for the tabular data
• Exploiting Categorical Structure Using Tree-Based Methods
• A Visual Analytics System for Multi-model Comparison on Clinical Data Predictions
• Model-agnostic interpretation by visualization of feature perturbations
• Real-bogus scores for active anomaly detection
• Morphological Classification of Extragalactic Radio Sources Using Gradient Boosting Methods
• Asynch-SGBDT: Asynchronous Parallel Stochastic Gradient Boosting Decision Tree based on Parameters Server
• CatBoost model with synthetic features in application to loan risk assessment of small businesses
• Graph Neural Networks in Real-Time Fraud Detection with Lambda Architecture
• High throughput screening with machine learning
• Attention augmented differentiable forest for tabular data
• StructureBoost: Efficient Gradient Boosting for Structured Categorical Variables
• Imputing missing values with unsupervised random trees
• LiteMORT: A memory efficient gradient boosting tree system on adaptive compact distributions
• Coresets for Decision Trees of Signals
• Factorized MultiClass Boosting
• WildWood: a new Random Forest algorithm
• Data Quality Evaluation using Probability Models
• Towards Interaction Detection Using Topological Analysis on Neural Networks
• To Boost or not to Boost: On the Limits of Boosted Neural Networks
• agtboost: Adaptive and Automatic Gradient Tree Boosting Computations
• Automated Supervised Feature Selection for Differentiated Patterns of Care
• Booster: An Accelerator for Gradient Boosting Decision Trees
• Efficient Click-Through Rate Prediction for Developing Countries via Tabular Learning
• Computational analysis of laminar structure of the human cortex based on local neuron features
• Rehabilitating Homeless: Dataset and Key Insights
• Team voyTECH: User Activity Modeling with Boosting Trees
• From Cities to Series: Complex Networks and Deep Learning for Improved Spatial and Temporal Analytics*