Gradient Boosting Decision Tree介绍

Gradient Boosting Decision Tree（GBDT）是一种集成学习算法，它通过串行地训练弱分类器并将它们相互补充，从而构建一个强分类器。它使用梯度下降算法（Gradient Descent）来最小化损失函数，从而提高模型的整体性能。GBDT通常应用于将一组弱分类器组合成更强大的模型，以进行二分类和回归问题的预测。它可以通过使用不同的损失函数来满足许多不同的应用场景。

相关问题

gradient boosting decision tree

梯度提升决策树（Gradient Boosting Decision Tree）是一种集成学习算法，它通过迭代地训练决策树模型来提高预测准确度。在每一轮迭代中，模型会根据前一轮的预测误差来调整样本的权重，使得下一轮的模型能够更加关注之前预测错误的样本。同时，模型也会在每一轮迭代中加入一个新的决策树，以进一步提高预测准确度。梯度提升决策树在许多机器学习任务中都表现出了很好的性能，例如分类、回归和排序等。

GBDT算法近五年参考文献

以下是近五年GBDT算法的参考文献：

1. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of statistics, 29(5), 1189-1232.

2. Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).

3. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., ... & Liu, T. Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. In Advances in neural information processing systems (pp. 3146-3154).

4. Chen, T., He, T., Benesty, M., Khotilovich, V., & Tang, Y. (2019). Xgboost: Extreme gradient boosting. R package version, 0.90.

5. Huang, G., Cheng, Y., & Chen, C. (2018). Gradient boosting decision tree methods for high-dimensional classification and regression. Transactions on Intelligent Systems and Technology, 9(1), 1-24.

6. Li, T., Zhu, S., & Ogihara, M. (2018). Gradient boosting decision tree with random feature subspace and random instance subsampling. Neurocomputing, 275, 2073-2082.

7. Wang, J., Zhang, T., & Li, Y. (2018). Multi-view gradient boosting decision tree. In IJCAI (pp. 3410-3416).

8. Sun, Y., Liu, Y., Zhang, X., & Li, Z. (2020). Multi-branch gradient boosting decision tree for imbalanced data classification. Applied Soft Computing, 86, 105916.

9. Wang, M., Li, X., & Wang, Y. (2020). Gradient boosting decision tree based on optimal feature selection and parameter tuning. Expert Systems with Applications, 143, 113050.

10. Zhang, S., Zhou, J., & Zhang, P. (2020). Gradient boosting decision tree with adaptive learning rate and dropout regularization. Neurocomputing, 379, 118-126.