机器学习风控实践与发展

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2017/10/18 发布于 技术 分类

机器学习风控实践与发展

QCon  QCon2017 

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20. U LR (Logistic Regression) ANN (Neural Network) DT (Decision Tree) SVM (Support Vector Machine) NB (Naive Bayes) p d GBDT GBDT+DNN GBDT+LR gcForest RF t accuracy 0.99583 0.99884 0.99828 0.99914 0.99914 precision 0.94586 0.99322 0.99586 0.99674 0.99715 recall 0.97139 0.98239 0.96938 0.98590 0.98550 GBDT GBDT+DNN GBDT+LR gcForest RF accuracy 0.99509 \ 0.99595 0.99655 0.99650 precision 0.45871 \ 0.56588 0.91255 0.91498 recall 0.52143 \ 0.41633 0.24742 0.23299
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24. • Bayesian Learning for Neural Networks. Lecture notes in statistics. Springer, New York, Berlin, Paris, 1996. ISBN 0-387-94724-8. • Practical variational inference for neural networks. In Advances in Neural Information Processing Systems 24, pages 2348–2356, 2011. • Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. In Proceedings of the 33rd International Conference on Machine Learning, 2016 • Manifold Gaussian processes for regression. In International Joint Conference on Neural Networks, 2016 • Stochastic variational deep kernel learning. In Advances in Neural Information Processing Systems, pages 2586–2594, 2016. • Deep kernel learning. In Arthur Gretton and Christian C. Robert, editors, Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, volume 51 of Proceedings of Machine Learning Research, pages 370–378, Cadiz, Spain,09–11 May 2016. PMLR.
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