论文标题
AgtBoost：自适应和自动梯度树的增强计算
agtboost: Adaptive and Automatic Gradient Tree Boosting Computations
论文作者
论文摘要
AgtBoost是一个R软件包，以类似于其他已建立的框架（例如XGBoost和LightGBM）的方式实现快速梯度树的计算，但计算时间和所需的数学和技术知识的大幅下降。该软件包会自动处理拆分/无分解的决策，并选择梯度树增强合奏中的树数，即AgtBoost会自动适应数据中的信息。所有这些都是在一次训练过程中完成的，这是通过利用树算法的信息理论中的发展{\ tt arxiv：2008.05926v1 [stat.me]}而成为可能的。 AgtBoost还具有特征重要的功能，可以消除插入噪声特征的常见实践。此外，有用的模型验证功能对学习分布执行了Kolmogorov-Smirnov测试。
agtboost is an R package implementing fast gradient tree boosting computations in a manner similar to other established frameworks such as xgboost and LightGBM, but with significant decreases in computation time and required mathematical and technical knowledge. The package automatically takes care of split/no-split decisions and selects the number of trees in the gradient tree boosting ensemble, i.e., agtboost adapts the complexity of the ensemble automatically to the information in the data. All of this is done during a single training run, which is made possible by utilizing developments in information theory for tree algorithms {\tt arXiv:2008.05926v1 [stat.ME]}. agtboost also comes with a feature importance function that eliminates the common practice of inserting noise features. Further, a useful model validation function performs the Kolmogorov-Smirnov test on the learned distribution.
