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Gradient boosting machines
Published in Brandon M. Greenwell, Tree-Based Methods for Statistical Learning in R, 2022
While XGBoost and LightGBM seem to be the most popular implementations of GBMs, they didn't initiallym handle categorical variables as well as another GBM variant called CatBoost [Dorogush et al., 2018; Prokhorenkova et al., 2017]. One of the main selling points of CatBoost is the ability to handle categorical variables without the need for numerically encoding them. From the CatBoost website: Improve your training results with CatBoost that allows you to use non-numeric factors, instead of having to pre-process your data or spend time and effort turning it to numbers.https://catboost.ai/
Ensemble Methods The Wisdom of the Crowd
Published in Chong Ho Alex Yu, Data Mining and Exploration, 2022
There are three major variants of boosting: gradient boost, XGBoost, and AdaBoost. New boosting methods, such as LightGBM and CatBoost, have recently emerged in the analytical market. However, their history is too short for a comprehensive evaluation, and results from benchmark studies comparing LightGBM, CatBoost, and XGBoost are mixed (Hancock and Khoshgoftaar 2020). Further, CatBoost, as its name implies, is for analyzing categorical data only. Hence, in this chapter only gradient boost, XGBoost, and AdaBoost are discussed.
Severity modeling of work zone crashes in New Jersey using machine learning models
Published in Journal of Transportation Safety & Security, 2023
Ahmed Sajid Hasan, Md Asif Bin Kabir, Mohammad Jalayer, Subasish Das
The objective of the boosting methods is to improve the prediction performance by combining a set of weak classifiers with a strong classifier. Three different boosting methods, such as XGboost, LightGBM, and Catboost, were used in this study The gradient boosting approach adjusts the losses by regressing the gradient vector function at each iteration (Friedman, 2001). Starting with the weak decision tree, which was used as the foundation decision Tree, a gradient boosting model adjusts the sequence of each decision tree. XGboost is a slow boosting strategy that reduces misclassification errors at each iteration by using sequential model training. LightGBM is a boosting method based on the development of more accurate and complex decision trees leaf-by-leaf. Catboost is a boosting approach that works with both numerical and category input variables. It takes care of the variables during the training period, which saves time on preprocessing.
A hybrid ensemble learning-based prediction model to minimise delay in air cargo transport using bagging and stacking
Published in International Journal of Production Research, 2022
Rosalin Sahoo, Ajit Kumar Pasayat, Bhaskar Bhowmick, Kiran Fernandes, Manoj Kumar Tiwari
CatBoost is an improved variant of the algorithm for gradient boosting decision trees explicitly developed to accommodate categorical features. It uses binary decision trees as base predictors. While training this bosting algorithm, the data is randomly shuffled and reshuffled multiple times to calculate the mean for every object only on its historical data. CatBoost uses oblivious decision trees, which use the same splitting criteria over the entire tree level. Let us consider the dataset , a differentiable loss function , and the number of iterations to be N. the boosting technique aims to search for an approximation to a function that minimises the loss function. Gradient boosting has little impact on the sample distribution since weak learners train on a strong learner’s residual errors (i.e., pseudo-residuals). The mapping function is termed as is to minimise the loss function with a constant value is as follows:
Enhancing flood risk assessment through integration of ensemble learning approaches and physical-based hydrological modeling
Published in Geomatics, Natural Hazards and Risk, 2023
Mohamed Saber, Tayeb Boulmaiz, Mawloud Guermoui, Karim I. Abdrabo, Sameh A. Kantoush, Tetsuya Sumi, Hamouda Boutaghane, Tomoharu Hori, Doan Van Binh, Binh Quang Nguyen, Thao T. P. Bui, Ngoc Duong Vo, Emad Habib, Emad Mabrouk
The CatBoost model is another enhanced boosting decision-tree learning technique proposed by (Dorogush et al. 2018). It employs a gradient-boosting scheme to construct a regression model through adjusted estimation. Furthermore, various refinements were performed to minimize the overfitting of the model. The gradient boosting model is a useful ML tool that has yielded accurate results in many disciplines, including environmental parameter estimation, geospatial ecosystem factor dispersion, and meteorological forecasting. The CatBoost model operates well in terms of categorical attributes. Typically, the absence of definite characteristics increases the accuracy of the model. It is primarily dependent on the use of gradient boosting, which employs a binary-tree classification scheme. The following points outline the differences between CatBoost and the other boosting techniques. A sophisticated method was incorporated to convert category characteristics into numerical information. As mentioned by (Prokhorenkova et al. 2018), target statistics are very effective for dealing with categorical attributes with minimal information errors.CatBoost combines categorical variables to take advantage of the existing relationship between different parameters.To reduce the overfitting problem and improve the classification performance, a symmetrical tree strategy is used.