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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/152775


    Title: 機器學習方法於分類或預測問題之比較與應用
    Machine Learning Methods in Classification or Prediction: Some Comparison and Applications
    Authors: 古政弘
    Gu, Cheng-Hung
    Contributors: 張育瑋
    Chang, Yu-Wei
    古政弘
    Gu, Cheng-Hung
    Keywords: 分類迴歸樹
    貝氏可加性迴歸樹
    隨機森林
    Classification and Regression Tree
    Bayesian Additive Regression Trees
    Random Forest
    Date: 2024
    Issue Date: 2024-08-05 13:59:17 (UTC+8)
    Abstract: 近年新的機器學習方法相當蓬勃發展，根據其應變數為連續型或類別型，這
    些方法可以被應用於預測或分類問題中。本研究感興趣一些機器學習方法的預測或分類準確度為何，並且特別聚焦於可解釋性的機器學習，因為在實際資料分析中，應用者也常會感興趣自變數與應變數的關係之解釋。在此考慮七種機器學習方法或統計方法：分類迴歸樹(Classification and Regression Tree)、貝氏可加性迴歸樹(Bayesian additive regression trees)、隨機森林(random forest)、多變量適應性迴歸弧線(multivariate adaptive regression splines)、廣義相加模型(generalized additive model)、線性判別分析(linear discriminant analysis)及二次判別分析 (quadratic discriminant analysis)，將這些方法分別應用至兩筆實際資料，對於資料的訓練集進行建模，比較各種方法在測試資料集之預測或分類效果。
    In recent years, a multitude of machine learning methods have been proposed.Depending on whether the response variable is continuous or ordinal categorical, these methods can be applied to prediction or classification problems. This study is interested in the predictive or classification accuracy of various machine learning methods, with a particular focus on interpretable machine learning. In practical data analysis, users often seek to understand the relationships between independent and dependent variables.We consider seven machine learning and statistical methods: Classification and Regression Tree, Bayesian Additive Regression Trees, Random Forest, Multivariate Adaptive Regression Splines, Generalized Additive Model, Linear Discriminant Analysis, and Quadratic Discriminant Analysis. We apply these methods to two real datasets. Subsequently, we compare the prediction and classification performance of the seven methods on the test sets.
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    Friedman, J. H. (1991). Multivariate Adaptive Regression Splines. Annals of Applied Statistics, 19, 1-67.
    Kim, C., & Park, S. (2022). Comparison of Tree-Based Ensemble Models for Regression. Communications for Statistical Applications and Methods, 29, 561-589.
    Knežević, marinela., Has, A., & Zekic´ -sušac, M. (2021). Predicting EnergyCost of Public Buildings by Artificial Neural Networks, CART, and Random Forest. Neurocomputing, 439, 223-233.
    Barros, F., Carvalho, G. C., Costa, Y., & Martins, I. (2022). Sea-Level RiseEffects on Macrozoobenthos Distribution within an Estuarine Gradient Using Species
    Distribution Modeling. Ecological Informatics, 71, 101816.
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    weights-of-evidence models for forest fire susceptibility mapping in China. Arab J Geosci 10, 167.
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    Description: 碩士
    國立政治大學
    統計學系
    111354005
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0111354005
    Data Type: thesis
    Appears in Collections:[Department of Statistics] Theses

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