Understanding and Improving Predictors

最近在预测方面的一个重要进展是发现，获得多个版本的预测器，然后将这些预测器组合起来可以大大降低错误率。例如bagging（Breiman）和Adaboost（Freund和Schapire）。目前尚不清楚的是这些减少背后的机制，这是我们研究中的一个主要问题。Shang和Breiman的工作表明，有可能生长出比标准方式（即CART或C4.5）生长的树具有相当低的错误率的二叉树。 该方法使用训练集来估计输入输出分布，然后使用该分布来生长树。 有必要进行更多的研究，使其强大和有效。目前，复杂的医学研究是使用一类简单的模型进行分析，这些模型依赖于输入变量的单个线性组合中的参数估计。另一个研究方向是将机器学习中开发的更一般的预测方法应用于医疗数据分析。这项研究将导致改进的预测方法，结合许多预测，在增长一个单一的二叉树，并在医疗结果的分析。

An important recent advance in prediction has been the discovery that getting multiple versions of a predictor and then combining these can lead to dramatic reductions in error rates. Examples are bagging (Breiman) and Adaboost (Freund and Schapire). What is not clear is the mechanism behind these reductions, and this is a main problem in our research. Work by Shang and Breiman show that it is possible to grow binary trees that have considerably lower error rates than trees grown the standard way, i.e. CART or C4.5. This method uses the training set to estimate the input-output distribution and then uses this distribution to grow the tree. More research is necessary to make it robust and efficient. Currently, complex medical studies are analyzed using a class of simple models that depend on estimating parameters in a single linear combination of the input variables. Another research direction is the adaptation of the more general predictive methods developed in machine learning to the analysis of medical data. This research will result in improved prediction methods in combining many predictors, in growing a single binary tree, and in the analysis of medical outcomes.