A Study of Machine Learning Algorithms to Measure The Feature Importance in Class-Imbalance Data Of Food Insecurity Cases in Indonesia

Abstract

The development of various machine learning algorithms on supervised models has become one of the issues in selecting a suitable algorithm. The black box of machine learning requires a technique that can be used to interpret the feature importance using the SHAP in order to obtain predictors. The class-imbalance problem in real cases is another challenge in improving the performance of minority class predictions. This study uses a food insecurity dataset, one of the SDG's important indicators to study to achieve zero hunger. The machine learning algorithms studied consisted of Random Forest, XGBoost, SVM, and NN. Meanwhile, the study of the effect of class imbalance used three treatments: without handling, SMOTE-N, and ADASYN-N. Twelve models are built based on a combination of four algorithms and three treatments to study the performance models and their feature importance. The SMOTE-N and ADASYN-N were able to increase the sensitivity value up to 0.48 units higher when compared