PERFORMANCE OF RIDGE LOGISTIC REGRESSION AND DECISION TREE IN THE BINARY CLASSIFICATION

In everyday life, we are always faced with making decisions to choose the right decision between 2 choices of decision candidates. The development of high-performance binary classification models is a challenge for researchers in the modeling field. Deployment both of logistic regression and decision tree model use the dataset having predictor features which are a mixture of categorical and numerical features, both models tend to suffer an overfitting problem. This study has the aim of building a ridge logistic regression and decision tree model on a dataset that has all features of a binary categorical scale. The novelty of this study is to observe the distribution of the two classes in the dataset using the transformation of principal components and linear discriminant projections and also to explore the importance of feature that plays a role in building the decision tree model. The ridge logistic regression model has an accuracy performance of 84% which is better than the decision tree model having an accuracy performance of 81%. There are only 2 features in the dataset dominating around 80% of the feature importance.