Decision trees have been widely used for many years in the statistical literature as powerful, effective, and easily interpretable classification algorithms that are able to automatically select relevant features. This thesis examines in detail both the tree growing phase and the underlying statistical analysis. In addition, various splitting algorithms and stopping rules are explained explicitly to obtain the most powerful tree model. This paper also utilizes numerous techniques to evaluate the accuracy of model. Moreover, the main idea of boosting and its corresponding algorithms (such as AdaBoost, AdaBoost.M1 and AdaBoost.MH) that are used to improve the accuracy of model are also discussed in this paper. Finally, we present two different applications to illustrate how decision tree and boosting method apply to live data.
|School:||California State University, Long Beach|
|School Location:||United States -- California|
|Source:||MAI 49/05M, Masters Abstracts International|
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