We describe a new deep learning model-Convolutional eXtreme Gradient Boosting
(ConvXGB) for classification problems based on convolutional neural nets and Chen et al.'s …

Description Extreme Gradient Boosting, which is an efficient implementation of the gradient
boosting framework from Chen & Guestrin (2016)< doi: 10.1145/2939672.2939785>. This …

A novel gradient boosting framework is proposed where shallow neural networks are
employed as``weak learners''. General loss functions are considered under this unified …

It is already reported in the literature that the performance of a machine learning algorithm is
greatly impacted by performing proper Hyper-Parameter optimization. One of the ways to …

Convolutional neural networks (CNNs), which are composed of multiple processing layers to
learn the representations of data with multiple abstract levels, are the most successful …

The boosting model is a kind of ensemble learning technology, including XGBoost and
GBDT, which take decision trees as weak classifiers and achieve better results in …

Over the last decade, substantial advances have been made in various computer vision
technologies and many of them are based on convolutional neural network (CNN) …

The family of gradient boosting algorithms has been recently extended with several
interesting proposals (ie XGBoost, LightGBM and CatBoost) that focus on both speed and …

Get to grips with building robust XGBoost models using Python and scikit-learn for
deployment Key Features Get up and running with machine learning and understand how to …

In this paper we present CatBoost, a new open-sourced gradient boosting library that
successfully handles categorical features and outperforms existing publicly available …