CatBoost: unbiased boosting with categorical features
Explore this paper's citation graph
Summary
This paper presents the key algorithmic techniques behind CatBoost, a new gradient boosting toolkit and provides a detailed analysis of this problem and demonstrates that proposed algorithms solve it effectively, leading to excellent empirical results.
- Type
- article
- Published
- 2017-06-28
- Cited by
- 6,485
- References
- 34
- Access
- Open access
- OpenAlex
- https://openalex.org/W2734096527
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:5044218
Keywords
Boosting (machine learning), Implementation, Gradient boosting, Computer science, Categorical variable
References
- Oblivious Decision Trees, Graphs, and Top-Down Pruning
- OUT-OF-BAG ESTIMATION
- Special Invited Paper-Additive logistic regression: A statistical view of boosting
- Estimating Probabilities: A Crucial Task in Machine Learning
- Using Iterated Bagging to Debias Regressions
- Greedy function approximation: A gradient boosting machine.
- Simple and Scalable Response Prediction for Display Advertising
- A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems
- Classification and regression trees
- Stochastic gradient boosting
- Practical Lessons from Predicting Clicks on Ads at Facebook
- Large Scale Online Learning
- A gradient boosting method to improve travel time prediction
- Boosting Algorithms as Gradient Descent
- An empirical comparison of supervised learning algorithms
- Sparse Online Learning via Truncated Gradient
- Cryptographic limitations on learning Boolean formulae and finite automata
- Top-down induction of decision trees classifiers - a survey
- Domain Adaptation under Target and Conditional Shift
- Adapting boosting for information retrieval measures
Cited by
- Gradient Boosting With Piece-Wise Linear Regression Trees
- RapidScorer: Fast Tree Ensemble Evaluation by Maximizing Compactness in Data Level Parallelization
- PaloBoost: An Overfitting-robust TreeBoost with Out-of-Bag Sample Regularization Techniques
- Gradient and Newton Boosting for Classification and Regression
- Brain age prediction of healthy subjects on anatomic MRI with deep learning : going beyond with an “explainable AI” mindset
- CatBoost: gradient boosting with categorical features support
- An integrative machine learning approach for prediction of toxicity-related drug safety
- Building Recommender System for Media with High Content Update Rate
- Efficient logic architecture in training gradient boosting decision tree for high-performance and edge computing
- ThunderGBM: Fast GBDTs and Random Forests on GPUs
- Heuristic Active Learning for the Prediction of Epileptic Seizures Using Single EEG Channel
- Classification of virtual patent marking web-pages using machine learning techniques
- Application of adaptive boosting (AdaBoost) in demand-driven acquisition (DDA) prediction: A machine-learning approach
- Constraint learning based gradient boosting trees
- KTBoost: Combined Kernel and Tree Boosting
- The comparison of pattern recognition algorithms for semantic image analysis
- Block-distributed Gradient Boosted Trees
- Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions
- Explainable AI for Trees: From Local Explanations to Global Understanding
- Bridging the Gap between Energy Consumption and Distribution through Non-Technical Loss Detection
Related papers
- LightGBM: A Highly Efficient Gradient Boosting Decision Tree
- Theoretical and Empirical Analysis of a Spatial EA Parallel Boosting Algorithm
- Theoretical and Empirical Analysis of a Parallel Boosting Algorithm
- A general framework for boosting feature subset selection algorithms
- Click-Through Rate Prediction Using Feature Engineered Boosting Algorithms
- Boosting instance selection algorithms
- Automatic Gradient Boosting