A working guide to boosted regression trees
A working guide to boosted regression trees
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DOI：
10.1111/j.1365-2656.2008.01390.x
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发表时间：
2008-07-01
影响因子：
4.8
通讯作者：
Hastie, T.
中科院分区：
文献类型：
--
作者：
Elith, J.;Leathwick, J. R.;Hastie, T.
1.生态学家使用统计模型进行解释和预测，并且需要足够灵活的技术来表达他们的数据的典型特征，例如非线性和相互作用。这项研究为提升回归树（BRT）提供了一个工作指南，这是一种用于拟合统计模型的集成方法，与旨在拟合单个简约模型的传统技术有着根本的不同。提升回归树联合收割机结合了两种算法的优势：回归树（通过递归二进制分割将响应与其预测因子关联的模型）和提升（一种自适应方法，用于组合许多简单模型以提高预测性能）。最终的BRT模型可以被理解为一个加性回归模型，其中各个项是简单的树，以向前的、逐步的方式拟合。提升回归树结合了基于树的方法的重要优点，处理不同类型的预测变量并容纳缺失数据。它们不需要事先进行数据转换或剔除离群值，可以拟合复杂的非线性关系，并自动处理预测变量之间的交互作用。在BRT中拟合多棵树克服了单树模型的最大缺点：它们相对较差的预测性能。虽然BRT模型很复杂，但它们可以以提供强大生态洞察力的方式进行总结，其预测性能上级大多数传统建模方法。快速公交系统的独特性给模型拟合带来了一些实际问题。我们证明了使用BRT的实用性和优势，通过分布分析的短鳍鳗鱼（安圭拉australis理查森），新西兰的本地淡水鱼。我们使用的数据集超过13 000个网站来说明几个设置的影响，然后拟合和解释模型使用的数据的子集。我们提供代码和教程，使生态学家更广泛地使用BRT。
1. Ecologists use statistical models for both explanation and prediction, and need techniques that are flexible enough to express typical features of their data, such as nonlinearities and interactions.2. This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fit a single parsimonious model. Boosted regression trees combine the strengths of two algorithms: regression trees (models that relate a response to their predictors by recursive binary splits) and boosting (an adaptive method for combining many simple models to give improved predictive performance). The final BRT model can be understood as an additive regression model in which individual terms are simple trees, fitted in a forward, stagewise fashion.3. Boosted regression trees incorporate important advantages of tree-based methods, handling different types of predictor variables and accommodating missing data. They have no need for prior data transformation or elimination of outliers, can fit complex nonlinear relationships, and automatically handle interaction effects between predictors. Fitting multiple trees in BRT overcomes the biggest drawback of single tree models: their relatively poor predictive performance. Although BRT models are complex, they can be summarized in ways that give powerful ecological insight, and their predictive performance is superior to most traditional modelling methods.4. The unique features of BRT raise a number of practical issues in model fitting. We demonstrate the practicalities and advantages of using BRT through a distributional analysis of the short-finned eel (Anguilla australis Richardson), a native freshwater fish of New Zealand. We use a data set of over 13 000 sites to illustrate effects of several settings, and then fit and interpret a model using a subset of the data. We provide code and a tutorial to enable the wider use of BRT by ecologists.