@phdthesis{digilib71765,
           month = {May},
           title = {IMPLEMENTASI REGRESI LOGISTIK BINER DENGAN REGULARISASI LASSO DALAM MENANGANI MASALAH OVERFITTING (STUDI KASUS: KANKER PAYUDARA WISCONSIN)},
          school = {UIN SUNAN KALIJAGA YOGYAKARTA},
          author = {NIM.: 21106010036 Sindi Lestari},
            year = {2025},
            note = {Sri Utami Zuliana, S.Si., M.Sc., Ph.D.},
        keywords = {LASSO, Overfitting, Penalti, Regresi Logistik Biner, Regularisasi},
             url = {https://digilib.uin-suka.ac.id/id/eprint/71765/},
        abstract = {Binary logistic regression is often used to predict variables with two possible
outcomes. In application, overfitting can occur when the model overfits the training
data and fails to provide accurate predictions on new data. To overcome overfitting,
regularization techniques can be used such as the LASSO method which adds
a penalty function to the size of the regression coefficients. This study aims to find
out how the application of LASSO method helps to reduce overfitting and improve
the ability of model to deal with new data. With the LASSO method, some regression
parameters will be penalized, so that their coefficient values become smaller or
even zero, which makes the model simpler and less complicated. The results show
that applying the LASSO method to the binary logistic regression model improves
the accuracy and generalization ability of the model. Before the application of the
LASSO method, the accuracy on the training data reached 100\%, while on the test
data was only 96\%. After applying LASSO, the accuracy on the test data increased
to 98,23\%, while the accuracy on the training data decreased to 98,24\%, which indicates
that the model becomes more balanced and no longer adjusts too much to
the training data. The LASSO method also simplifies the model by eliminating irrelevant
variables and producing a more efficient model}
}