SAS 决策树 bagging

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<p>我在用SAS EM做决策树的bagging 和boosting. 有谁知道bagging 的输出是什么意思(请看附件),里面N,Mean,MAX, MIN 是什么，多谢啊</p><p>还有，对于决策树来说，是不是testing data没有意义？validation data是否就是testing? </p><p></p><p></p><p></p> 196009.pdf (18.11 KB)

2008-3-6 06:38:00 上传

SAS 决策树 bagging

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关键词：bagging bag ING 决策树 Validation SAS 决策树 bagging

<p>Bagging is the ensembl modeling. Bagging combines the several models and take the&nbsp;weighted or unweighted average, voting estimation of&nbsp;all models.&nbsp; The more different models are, the more powerful the Bagging is. Therefore, Bagging is normally based on Decision Tree, which is unstable modeling method.</p><p>In your output, Bagging generates 10 trees(assuming you use tree), and n, min, max , mean show the statisitics of 10 trees. , i.e the minimum misclassification rate for validation dataset is 0.1746. </p><p>Testing dataset is optional for model buidling because validation dataset can help you tune and select the optimal model. However, if you want to verify your model then you can use testing dataset to get the unbiased prediction to check it. Particularly, when your dataset is small, it is not wise to partition part of your dataset as testing dataset.</p><p>I have one question, are you using EM 4.0 to get bagging? or EM 5.3. I am sure in Em5.2, there is no bagging, boosting and randomforest.</p>

<p>Hi, thanks for your answer. I'm using EM4.3. by the way, where can I finding the&nbsp;final classification error&nbsp;from bagging? It seems that it should take majority voting as the final prediction result. Does it show&nbsp;as the classification table in my output ? Also, do you know how to do bootstrap for decision tree using EM ? Is bootstrap and cross validation used for model selection also? </p>

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