Çoklu Doğrusal Bağlantılı Nadir Olayların Modellenmesinde Lasso ve Ridge Regresyon ile Boosting Algoritmalarının Performans Karşılaştırması

Year 2024, Volume: 9 Issue: 1, 154 - 166, 29.06.2024

Olcay Alpay

https://doi.org/10.33484/sinopfbd.1434260

Abstract

Bu çalışma, iki durumlu olayları modellemek için kullanılan makine öğrenmesi tekniklerinde karşılaşılan nadirlik ve “çoklu doğrusal bağlantı” ya da sadece “çoklu bağlantı” olarak tanımlanan sorunu ele alınmaktadır. Çoklu doğrusal bağlantı (ÇDB), bağımsız değişkenler arasında bir ya da birden fazla kuvvetli doğrusal bağımlılık olma durumudur ve bir sorun olarak ortaya çıkar. Üzerinde çalışılan veri içerisinde çoklu doğrusal bağlantı probleminin var olması regresyon katsayılarının varyanslarının büyümesi gibi olumsuz bir sonuca sebebiyet verir. Bu çalışmada, Lasso ve Ridge Regresyon ile GradientBoost, XGBoost, LightGBM ve AdaBoost gibi artırma algoritmaları içeren düzenleme ve ölçeklendirme tekniklerinin, çoklu doğrusal bağlantılı nadir olayların modellenmesinde, algoritmaların performanslarını karşılaştırmak için detaylı bir simülasyon çalışması sunulmaktadır. Simülasyon çalışmasında, verideki dengesizliği ortadan kaldırmak amacıyla yeniden örnekleme yöntemleri kullanılarak sonuçlara etkisi Hata Kareler Ortalaması (HKO), R^2, Hassasiyet (Precision-Prec), Duyarlılık (Recall-Rec) ve Eğri Altında Kalan Alan (Area Under the Curve-AUC) gibi performans metrikleri ve İşlem Karakteristik Eğrisi (Receiver Operating Characteristic- ROC) grafikleri ile araştırılmaktadır. Sonuçlar Lasso, Ridge ve Boosting algoritmalarının ÇDB’ya sahip nadir olayların modellenmesinde hangi yöntemin uygun olduğunu belirlemek açısından katkı sunmaktadır.

Keywords

Lasso regresyon, Ridge regresyon, Boosting algoritmaları, performans metrikleri, yeniden örnekleme teknikleri

Performance Comparison of Lasso and Ridge Regression and Boosting Algorithms for Modeling Rare Events with Multicollinearity

Abstract

This study examines the issues of rarity and multicollinearity in machine learning techniques used to model binary events. Multicollinearity (MC) is the presence of strong linear dependencies among independent variables, which poses a problem. In the context of the data being studied, the existence of multicollinearity leads to undesired consequences such as an enlargement of the variances of the regression coefficients. This study presents a simulation comparing the performance of algorithms in modelling multicollinear and rare events. Regularization and scaling techniques such as Lasso and Ridge Regression, as well as Boosting algorithms like GradientBoost, XGBoost, LightGBM, and AdaBoost are utilized. The impact of resampling methods to reduce data imbalance is also investigated using performance metrics such as Mean Squared Error (MSE), R^2, Precision (Prec), Recall (Rec) and AUC values, along with ROC curves. The results help to determine the appropriate method for modelling rare events with multicollinearity and provide insight into the performance of Lasso, Ridge and Boosting algorithms.

Keywords

Lasso regression, Ridge regression, Boosting algorithms, performance metrics, resampling techniques

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