Stacked Ensemble Models for Improved Prediction Accuracy
Stacked Ensemble Models for Improved Prediction Accuracy
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发表时间：
2017
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通讯作者：
R. Wolfinger;Pei-Yi Tan
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作者：
R. Wolfinger;Pei-Yi Tan
包围建模现在是一种完善的提高预测准确性的方法;它使您能够从不同的模型中平均噪声，从而增强可概括的信号。基本的堆叠集成技术联合收割机组合来自多个机器学习算法的预测，并将这些预测用作第二级学习模型的输入。本文展示了如何通过各种方法（如森林、梯度提升决策树、因子分解机和逻辑回归）生成一组不同的模型，然后将它们与SAS可视化数据挖掘和机器学习中的堆叠集成技术（如爬山、梯度提升和非负最小二乘）相结合。将这些技术应用于现实世界的大数据问题，展示了使用堆叠集成如何比单个模型产生更高的预测准确性和鲁棒性。这种方法足够强大，足以改变您最初的数据挖掘思维方式，从寻找单一的最佳模型到寻找一系列非常好的互补模型。由于训练大量模型和正确使用交叉验证以避免过拟合，它确实涉及额外的成本。本文展示了如何有效地处理这种计算费用在现代SAS环境中，以及如何管理一个合奏工作流程，在分布式框架中使用并行计算。
Ensemble modeling is now a well-established means for improving prediction accuracy; it enables you to average out noise from diverse models and thereby enhance the generalizable signal. Basic stacked ensemble techniques combine predictions from multiple machine learning algorithms and use these predictions as inputs to second-level learning models. This paper shows how you can generate a diverse set of models by various methods such as forest, gradient boosted decision trees, factorization machines, and logistic regression and then combine them with stacked-ensemble techniques such as hill climbing, gradient boosting, and nonnegative least squares in SAS Visual Data Mining and Machine Learning. The application of these techniques to real-world big data problems demonstrates how using stacked ensembles produces greater prediction accuracy and robustness than do individual models. The approach is powerful and compelling enough to alter your initial data mining mindset from finding the single best model to finding a collection of really good complementary models. It does involve additional cost due both to training a large number of models and the proper use of cross validation to avoid overfitting. This paper shows how to efficiently handle this computational expense in a modern SAS environment and how to manage an ensemble workflow by using parallel computation in a distributed framework.