We are analyzing https://link.springer.com/article/10.1007/s10462-020-09896-5.

Title:
A comparative analysis of gradient boosting algorithms | Artificial Intelligence Review
Description:
The family of gradient boosting algorithms has been recently extended with several interesting proposals (i.e. XGBoost, LightGBM and CatBoost) that focus on both speed and accuracy. XGBoost is a scalable ensemble technique that has demonstrated to be a reliable and efficient machine learning challenge solver. LightGBM is an accurate model focused on providing extremely fast training performance using selective sampling of high gradient instances. CatBoost modifies the computation of gradients to avoid the prediction shift in order to improve the accuracy of the model. This work proposes a practical analysis of how these novel variants of gradient boosting work in terms of training speed, generalization performance and hyper-parameter setup. In addition, a comprehensive comparison between XGBoost, LightGBM, CatBoost, random forests and gradient boosting has been performed using carefully tuned models as well as using their default settings. The results of this comparison indicate that CatBoost obtains the best results in generalization accuracy and AUC in the studied datasets although the differences are small. LightGBM is the fastest of all methods but not the most accurate. Finally, XGBoost places second both in accuracy and in training speed. Finally an extensive analysis of the effect of hyper-parameter tuning in XGBoost, LightGBM and CatBoost is carried out using two novel proposed tools.
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Keywords {🔍}

article, google, scholar, boosting, gradient, learning, data, algorithms, machine, xgboost, analysis, information, lightgbm, catboost, comparison, classification, privacy, cookies, content, research, search, martínezmuñoz, learn, math, res, mathscinet, publish, gonzalo, accuracy, access, optimization, mach, syst, tree, decision, liu, author, springer, processing, log, journal, published, bentéjac, csörgő, speed, training, performance, prediction, generalization, hyperparameter,

Topics {✒️}

/microsoft/lightgbm/tree/master/python-package ai/docs/concepts/python-installation bayesian hyper-parameter optimization gonzalo martínez-muñoz month download article/chapter competitiveness-state research agency liquefaction-induced lateral spreading particle swarm optimization supervised learning algorithms supervised machine learning machine learning models regression tree ensembles gradient boosting algorithms label learning approach martínez-muñoz privacy choices/manage cookies gradient boosting machine art classification algorithms full article pdf multiple data sets extreme gradient boosting stochastic gradient boosting related subjects high gradient instances author information authors gradient boosting work hyper-parameter setup hyper-parameter tuning 23rd international conference european economic area scalable ensemble technique decision forest friedman jh improve gradient boosting machine learning carefully tuned models statistical comparisons bright extragalactic objects gravitationally lensed quasars consensus vote models adaptive data analysis conditions privacy policy bioactive molecule prediction solar radiation prediction additional information publisher' article bentéjac accepting optional cookies data mining classification algorithms greedy function approximation

Questions {❓}

Fernández-Delgado M, Cernadas E, Barro S, Amorim D (2014) Do we need hundreds of classifiers to solve real world classification problems?

Schema {🗺️}

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         description:The family of gradient boosting algorithms has been recently extended with several interesting proposals (i.e. XGBoost, LightGBM and CatBoost) that focus on both speed and accuracy. XGBoost is a scalable ensemble technique that has demonstrated to be a reliable and efficient machine learning challenge solver. LightGBM is an accurate model focused on providing extremely fast training performance using selective sampling of high gradient instances. CatBoost modifies the computation of gradients to avoid the prediction shift in order to improve the accuracy of the model. This work proposes a practical analysis of how these novel variants of gradient boosting work in terms of training speed, generalization performance and hyper-parameter setup. In addition, a comprehensive comparison between XGBoost, LightGBM, CatBoost, random forests and gradient boosting has been performed using carefully tuned models as well as using their default settings. The results of this comparison indicate that CatBoost obtains the best results in generalization accuracy and AUC in the studied datasets although the differences are small. LightGBM is the fastest of all methods but not the most accurate. Finally, XGBoost places second both in accuracy and in training speed. Finally an extensive analysis of the effect of hyper-parameter tuning in XGBoost, LightGBM and CatBoost is carried out using two novel proposed tools.
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