Evaluation of Naive and Covariance Algorithms in Generalized Linear Models (GLMs) Khalilah Nurfadilah (a*), Khairil Anwar Notodiputro (b), Bagus Sartono (b), Vera Maya Santi (c), Rini Warti (d)
a) UIN Alauddin Makassar
JL. HM Yasin Limpo No. 36 Makasar
* khalilahnurfadilah1202[at]gmail.com
b) Departemen Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Institut Pertanian Bogor
Institut Pertanian Bogor, Jalan Raya Dramaga, Bogor, 16680, Indonesia
c) Departemen Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Jakarta
Jalan Rawamangun Muka, Kec. Pulo Gadung, Daerah Khusus Ibukota Jakarta, 13220, Indonesia
d)Program Studi Tadris Matematika UIN Sulthan Taha Syaifuddin Jambi
Jalan Jambi-MA Bulian KM. 16 Muaro Jambi, Indonesia
Abstract
One of the important aspect in modeling is the simplicity of the model itself. Simplification of the model can be done by several methods, including the Ridge regression, LASSO and Elastic Net. In selecting the variables in these models an algorithm is developed, namely Naive and Covariance. Previous research revealed that the Covariance algorithm is superior in terms of time compared to the Naive algorithm. This is then evaluated by applying models and algorithms to male sex behavior data with the criteria of goodness, namely the simplicity of the model and the minimum AIC value. Based on the results of the study, it was found that the Covariance algorithm still outperformed the Naive algorithm in all three models
