Machine learning
XGBoost
XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.
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Sources
- Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI: 10.1145/2939672.2939785 ↗
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Active Learning Gradient BoostingActive Learning LightGBMAdaBoostAttention MechanismBaggingBayesian BoostingBayesian LightGBMBayesian XGBoostBERT Fine-TuningBidirectional RNNBoostingCatBoostCNN Image ClassificationConvolutional Neural NetworkCredit ScoringDecision TreeDeep Reinforcement LearningDilated CNNEnsemble Gradient BoostingExplainable Decision TreeExplainable Extra TreesExplainable Gradient BoostingExplainable LightGBMExplainable Random ForestExplainable Stacking EnsembleExplainable XGBoostExtra TreesGPT Fine-TuningGradient BoostingGraph Attention NetworkGraph Neural NetworkGRUKnowledge DistillationLightGBMLongformer / BigBirdLoRA and PEFTLSTMMixture of ExpertsMulti-layer PerceptronMultilayer PerceptronNeural Architecture SearchNeural ODEOnline Gradient BoostingRandom ForestRegularized BoostingRegularized CatBoostRegularized Gradient BoostingRegularized LightGBMRobust BoostingRobust Gradient BoostingRobust LightGBMRobust Random ForestRobust Stacking EnsembleRobust XGBoostSelf-AttentionSelf-supervised BoostingSelf-supervised Gradient BoostingSelf-supervised LightGBMSelf-supervised Random ForestSelf-supervised Stacking EnsembleSemi-supervised BoostingSemi-supervised Gradient BoostingSemi-supervised XGBoostSequence-to-Sequence ModelSHAPStackingStochastic Gradient DescentTextCNNTransformerVisual Contrastive Learning