Regularization parameter tuning optimization approach in logistic regression
Ahmed El-Koka, Kyung-Hwan Cha, Dae-Ki Kang · 2013
Abstract — Under regression analysis methods, logistic regression comes and it got popular since it has proved its effectiveness in modelling categorical outcomes as a function of either continuous-real value- or categorical-yes vs. no- variables. The coefficients of this prediction function are based on a data set that is used to shape this function. However, sometimes the dataset, which is used to generate the prediction function of the logistic regression, would have some odds and need to be smoothened to avoid under or over-fitting. Thus, a mathematical regularization part has been introduced to be added to the cost function of logistic regression and it mainly contains an important parameter which is called the regularization parameter that would have to be determined. Often, this regularization parameter is pre-set or pre-expected by the