XGBoost: Difference between revisions
No edit summary |
No edit summary |
||
Line 1: | Line 1: | ||
== Presented by == | == Presented by == | ||
Chun Waan Loke | * Chun Waan Loke | ||
* Peter Chong | |||
* Clarice Osmond | |||
* Zhilong Li | |||
== Introduction == | == Introduction == | ||
Line 15: | Line 18: | ||
== References == | == References == | ||
[1] R. Bekkerman. The present and the future of the kdd cup competition: an outsider’s perspective. | |||
[2] R. Bekkerman, M. Bilenko, and J. Langford. Scaling Up Machine Learning: Parallel and Distributed Approaches. Cambridge University Press, New York, NY, USA, 2011. | |||
[3] J. Bennett and S. Lanning. The netflix prize. In Proceedings of the KDD Cup Workshop 2007, pages 3–6, New York, Aug. 2007. | |||
[4] L. Breiman. Random forests. Maching Learning, 45(1):5–32, Oct. 2001. | |||
[5] C. Burges. From ranknet to lambdarank to lambdamart: An overview. Learning, 11:23–581, 2010. | |||
[6] O. Chapelle and Y. Chang. Yahoo! Learning to Rank Challenge Overview. Journal of Machine Learning Research - W & CP, 14:1–24, 2011. | |||
[7] T. Chen, H. Li, Q. Yang, and Y. Yu. General functional matrix factorization using gradient boosting. In Proceeding of 30th International Conference on Machine Learning | |||
(ICML’13), volume 1, pages 436–444, 2013. | |||
[8] T. Chen, S. Singh, B. Taskar, and C. Guestrin. Efficient | |||
second-order gradient boosting for conditional random fields. In Proceeding of 18th Artificial Intelligence and Statistics Conference (AISTATS’15), volume 1, 2015. | |||
[9] R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research, 9:1871–1874, 2008. | |||
[10] J. Friedman. Greedy function approximation: a gradient boosting machine. Annals of Statistics, 29(5):1189–1232, 2001. | |||
[11] J. Friedman. Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4):367–378, 2002. | |||
[12] J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: a statistical view of boosting. Annals of Statistics, 28(2):337–407, 2000. | |||
[13] J. H. Friedman and B. E. Popescu. Importance sampled learning ensembles, 2003. | |||
[14] M. Greenwald and S. Khanna. Space-efficient online computation of quantile summaries. In Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data, pages 58–66, 2001. | |||
[15] X. He, J. Pan, O. Jin, T. Xu, B. Liu, T. Xu, Y. Shi, | |||
A. Atallah, R. Herbrich, S. Bowers, and J. Q. n. Candela. Practical lessons from predicting clicks on ads at facebook. In Proceedings of the Eighth International Workshop on Data Mining for Online Advertising, ADKDD’14, 2014. | |||
[16] P. Li. Robust Logitboost and adaptive base class (ABC) Logitboost. In Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI’10), pages 302–311, 2010. | |||
[17] P. Li, Q. Wu, and C. J. Burges. Mcrank: Learning to rank using multiple classification and gradient boosting. In Advances in Neural Information Processing Systems 20, pages 897–904. 2008. | |||
[18] X. Meng, J. Bradley, B. Yavuz, E. Sparks, | |||
S. Venkataraman, D. Liu, J. Freeman, D. Tsai, M. Amde, S. Owen, D. Xin, R. Xin, M. J. Franklin, R. Zadeh, | |||
M. Zaharia, and A. Talwalkar. MLlib: Machine learning in apache spark. Journal of Machine Learning Research, 17(34):1–7, 2016. | |||
[19] B. Panda, J. S. Herbach, S. Basu, and R. J. Bayardo. Planet: Massively parallel learning of tree ensembles with mapreduce. Proceeding of VLDB Endowment, 2(2):1426–1437, Aug. 2009. | |||
[20] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, | |||
B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, | |||
R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, | |||
D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830, 2011. | |||
[21] G. Ridgeway. Generalized Boosted Models: A guide to the gbm package. | |||
[22] S. Tyree, K. Weinberger, K. Agrawal, and J. Paykin. Parallel boosted regression trees for web search ranking. In Proceedings of the 20th international conference on World wide web, pages 387–396. ACM, 2011. | |||
[23] J. Ye, J.-H. Chow, J. Chen, and Z. Zheng. Stochastic gradient boosted distributed decision trees. In Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM ’09. | |||
[24] Q. Zhang and W. Wang. A fast algorithm for approximate quantiles in high speed data streams. In Proceedings of the 19th International Conference on Scientific and Statistical Database Management, 2007. | |||
[25] T. Zhang and R. Johnson. Learning nonlinear functions using regularized greedy forest. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(5), 2014. |
Revision as of 15:02, 23 November 2021
Presented by
- Chun Waan Loke
- Peter Chong
- Clarice Osmond
- Zhilong Li
Introduction
Tree Boosting In A Nutshell
Split Finding Algorithms
System Design
End To End Evaluations
Conclusion
References
[1] R. Bekkerman. The present and the future of the kdd cup competition: an outsider’s perspective. [2] R. Bekkerman, M. Bilenko, and J. Langford. Scaling Up Machine Learning: Parallel and Distributed Approaches. Cambridge University Press, New York, NY, USA, 2011. [3] J. Bennett and S. Lanning. The netflix prize. In Proceedings of the KDD Cup Workshop 2007, pages 3–6, New York, Aug. 2007. [4] L. Breiman. Random forests. Maching Learning, 45(1):5–32, Oct. 2001. [5] C. Burges. From ranknet to lambdarank to lambdamart: An overview. Learning, 11:23–581, 2010. [6] O. Chapelle and Y. Chang. Yahoo! Learning to Rank Challenge Overview. Journal of Machine Learning Research - W & CP, 14:1–24, 2011. [7] T. Chen, H. Li, Q. Yang, and Y. Yu. General functional matrix factorization using gradient boosting. In Proceeding of 30th International Conference on Machine Learning (ICML’13), volume 1, pages 436–444, 2013. [8] T. Chen, S. Singh, B. Taskar, and C. Guestrin. Efficient second-order gradient boosting for conditional random fields. In Proceeding of 18th Artificial Intelligence and Statistics Conference (AISTATS’15), volume 1, 2015. [9] R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research, 9:1871–1874, 2008. [10] J. Friedman. Greedy function approximation: a gradient boosting machine. Annals of Statistics, 29(5):1189–1232, 2001. [11] J. Friedman. Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4):367–378, 2002. [12] J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: a statistical view of boosting. Annals of Statistics, 28(2):337–407, 2000. [13] J. H. Friedman and B. E. Popescu. Importance sampled learning ensembles, 2003. [14] M. Greenwald and S. Khanna. Space-efficient online computation of quantile summaries. In Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data, pages 58–66, 2001. [15] X. He, J. Pan, O. Jin, T. Xu, B. Liu, T. Xu, Y. Shi, A. Atallah, R. Herbrich, S. Bowers, and J. Q. n. Candela. Practical lessons from predicting clicks on ads at facebook. In Proceedings of the Eighth International Workshop on Data Mining for Online Advertising, ADKDD’14, 2014. [16] P. Li. Robust Logitboost and adaptive base class (ABC) Logitboost. In Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI’10), pages 302–311, 2010. [17] P. Li, Q. Wu, and C. J. Burges. Mcrank: Learning to rank using multiple classification and gradient boosting. In Advances in Neural Information Processing Systems 20, pages 897–904. 2008. [18] X. Meng, J. Bradley, B. Yavuz, E. Sparks, S. Venkataraman, D. Liu, J. Freeman, D. Tsai, M. Amde, S. Owen, D. Xin, R. Xin, M. J. Franklin, R. Zadeh, M. Zaharia, and A. Talwalkar. MLlib: Machine learning in apache spark. Journal of Machine Learning Research, 17(34):1–7, 2016. [19] B. Panda, J. S. Herbach, S. Basu, and R. J. Bayardo. Planet: Massively parallel learning of tree ensembles with mapreduce. Proceeding of VLDB Endowment, 2(2):1426–1437, Aug. 2009. [20] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830, 2011. [21] G. Ridgeway. Generalized Boosted Models: A guide to the gbm package. [22] S. Tyree, K. Weinberger, K. Agrawal, and J. Paykin. Parallel boosted regression trees for web search ranking. In Proceedings of the 20th international conference on World wide web, pages 387–396. ACM, 2011. [23] J. Ye, J.-H. Chow, J. Chen, and Z. Zheng. Stochastic gradient boosted distributed decision trees. In Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM ’09. [24] Q. Zhang and W. Wang. A fast algorithm for approximate quantiles in high speed data streams. In Proceedings of the 19th International Conference on Scientific and Statistical Database Management, 2007. [25] T. Zhang and R. Johnson. Learning nonlinear functions using regularized greedy forest. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(5), 2014.