Decision Tree Learning
Optimal Decision Tree with Noisy Outcomes
Su Jia, viswanath nagarajan, Fatemeh Navidi, R Ravi
Neural Information Processing Systems http://nips.cc/
ADebiasedMDIFeatureImportanceMeasurefor RandomForests
In particular, interpreting Random Forests (RFs) [2] and its variants [14, 28, 27, 29, 1, 12] has become an important area of research due to the wide ranging applications of RFs invarious scientific areas, such asgenome-wide association studies (GWAS)[7],gene expression microarray[13,23],andgeneregulatorynetworks[9].
Figure 1: Protein with random forest across 140 evaluations with different NN structure for distGP's
Thank you for all the reviewers time and effort. Thank you for your detailed review. Here, the idea is to re-train our model when new data is available. Here we explain our design space (see additional details in Appendix A.3, B and C); (i) Choice of embedding (joint vs Reviewer 3 Thank you for your review, and for comments regarding experiments, please see above. Thank you for your positive comments regarding the quality of the paper.
Minimal Variance Sampling in Stochastic Gradient Boosting
Differentsamplingapproaches were proposed, where probabilities are not uniform, and it is not currently clear which approach is the most effective. In this paper, we formulate the problem of randomization in SGB in terms of optimization of sampling probabilities to maximize the estimation accuracy of split scoring used to train decision trees.
