Comparative evaluation of penalized techniques and machine learning techniques for the study of linear model
Συγκριτική αξιολόγηση ποινικοποιημένων τεχνικών και τεχνικών μηχανικής μάθησης
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Keywords
Regression ; Tree models ; Regression trees ; Machine learning ; Penalized regression ; Random forestsAbstract
The prediction of continuous response variables is an important topic in modern statistical analysis and machine learning. In addition to classical linear models, penalized regression methods such as Ridge, Lasso, and Elastic Net have been developed to address problems including multicollinearity and dimensionality reduction. At the same time, regression tree methods provide a flexible non-parametric approach capable of modeling nonlinear relationships among variables, while modern techniques such as Random Forests combine multiple trees in order to improve predictive accuracy.
This thesis studies and compares the predictive performance of the aforementioned methods, as well as hybrid approaches that combine variable selection through Elastic Net with regression trees. Furthermore, generalized penalty functions for the tree pruning process are investigated in order to examine whether these generalizations can improve the predictive performance of the resulting models. The methods are evaluated using real-world datasets and appropriate performance measures, with the aim of highlighting the advantages and limitations of each approach.
The introductory chapter presents the two real-world datasets available in the R statistical environment.
Postgraduate Studies Programme
Εφαρμοσμένη ΣτατιστικήDepartment
Σχολή Χρηματοοικονομικής και Στατιστικής. Τμήμα Στατιστικής και Ασφαλιστικής ΕπιστήμηςNumber of pages
82Language
EnglishCollections
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Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα
Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα