Understanding and Improving Predictors

预测领域最近的一个重要进展是发现获得预测变量的多个版本然后将它们组合起来可以显着降低错误率。例如 bagging (Breiman) 和 Adaboost (Freund 和 Schapire)。目前尚不清楚这些减少背后的机制，这是我们研究的一个主要问题。 Shang 和 Breiman 的工作表明，与以标准方式（即 CART 或 C4.5）生长的树相比，生长二叉树的错误率要低得多。 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。 需要进行更多研究才能使其稳健且高效。目前，复杂的医学研究是使用一类简单模型来分析的，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通用的预测方法应用于医疗数据分析。这项研究将改进预测方法，包括组合许多预测变量、生成单个二叉树以及分析医疗结果。

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.