Turkiye Klinikleri Journal of Biostatistics

.: ORIGINAL RESEARCH
Evaluation of Traditional Machine Learning and Mixed Effect Machine Learning Model Performances by Simulation Study
Geleneksel Makine Öğrenmesi ve Karışık Etkili Makine Öğrenmesi Model Performanslarının Benzetim Çalışması ile Değerlendirilmesi
Ebru TURGALa
aDepartment of Biostatistics, Ankara University Faculty of Medicine, Ankara, Türkiye
Turkiye Klinikleri J Biostat. 2022;14(3):147-57
doi: 10.5336/biostatic.2022-91651
Article Language: EN
Full Text
ABSTRACT
 Objective: The aim of this study is to examine the effectiveness of linear mixed-effects (LME) model, one of the traditional models used in the classification of clustered data, and mixed-effects machine learning models, which are the latest approaches. Material and Methods: For the simulation, various data sets were created with different number of groups (250, 500, 1000) and different sample sizes (5000, 10000, 15000). Within the scope of the simulation, LME model, mixed-effects random forest (MERF) and Gaussian process boosting (GPBoost) models were compared in terms of root mean square error (RMSE) on two functions. Results: When the error variance (EV) is 4 for the linear function, sample size is small and the number of groups is high, RMSE of MERF model is smaller. In all other scenarios, RMSE of the linear model was smaller and. In cases where EV for the nonlinear function is 1, the sample size is small and the number of groups is high, RMSE of MERF is smaller. In all other scenarios (while EV was 1), RMSE of GPBoost model was small, p0.05 for MERF. In cases where EV is 4 for the nonlinear function and the sample size and groups is high, RMSE of MERF is smaller. In all other scenarios (while EV was 4), RMSE of GPBoost model was smaller. Conclusion: As a conclusion, for a nonlinear function, GPBoost performed better than MERF and LME methods in terms of RMSE and time. However, when a linear function is considered, LME gives a better result.

Keywords: Grouped data; machine learning; random effects; simulation
ÖZET
 Amaç: Bu çalışmanın amacı, kümelenmiş verilerin sınıflamasında kullanılan geleneksel modellerden doğrusal karışık etkili [linear mixed-effects (LME)] model ile karışık etkili makine öğrenmesi modellerinin etkinliklerini incelemektir. Gereç ve Yöntemler: Benzetim tekniği için farklı küme sayılarında (250, 500, 1000) ve farklı örneklem büyüklüklerinde (5000, 10000, 15000) çeşitli veri setleri oluşturulmuştur. Benzetim çalışması kapsamında LME model, karışık etkili rastgele orman [mixed-effects random forest (MERF)] ve Gauss süreci boosting [Gaussian process boosting (GPBoost)] yöntemlerinin hata kareler ortalamasının karekökü [root mean square error (RMSE)] değeri bakımından karşılaştırılması 2 fonksiyon üzerinde gerçekleştirmiştir. Bulgular: Doğrusal fonksiyon için hata varyansı 4, örneklem sayısı az ve küme sayısı fazla olduğunda, doğrusal model yerine MERF modelinin RMSE daha küçük bulunmuştur. Bunun haricindeki tüm senaryolarda doğrusal modelin RMSE değerinin küçük olduğu görülmüştür. Doğrusal olmayan fonksiyon için hata varyansı 1, örneklem sayısının küçük ve küme sayısının yüksek olduğu durumlarda, MERF modelinin RMSE değeri daha küçük bulunmuştur. Bunun haricindeki tüm senaryolarda (hata varyansı 1 iken) GPBoost modelinin RMSE değerinin küçük, LME ile arasındaki fark için p0,05 olduğu görülmüştür. Doğrusal olmayan fonksiyon için hata varyansı 4, örneklem ve küme sayısının yüksek olduğu durumlarda, MERF modeline ait RMSE daha küçük bulunmuştur. Bunun haricindeki tüm senaryolarda (hata varyansı 4 iken) GPBoost modelinin RMSE değeri küçük bulunmuştur. Sonuç: Sonuç olarak doğrusal olmayan bir fonksiyon için GPBoost; MERF ve LME yöntemine göre RMSE ve zaman açısından daha iyi bir performans göstermiştir. Ancak doğrusal bir fonksiyon ele alındığında LME daha iyi bir sonuç vermektedir.

Anahtar Kelimeler: Kümelenmiş veri; makine öğrenmesi; rastgele etki; benzetim
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