Beans classification using decision tree and random forest with randomized search hyperparameter tuning

Abstract

Dry-beans are a food with high protein. Dry-beans can be used as processed food products for emergency conditions such as famine, natural disasters, and war. Dry-beans can be used as a long-lasting product. To identify types of beans, manual work certainly requires a lot of time and effort. Therefore, creating a system that can classify beans in a computerized system is necessary. In this study, we classified beans using public data from Koklu. The data consists of sixteen features, seven classes with 13,611 rows. The data for each class of bean is unbalanced, so it is necessary to carry out a balanced dataset using random oversampling. Machine learning for classification using Decision Tree and Random Forest. Apart from that, hyperparameter tuning with randomize search for the number of trees 50, 75, 150, 200, and 300. The test results show that the Random Forest’s accuracy, precision, recall, and f1-score reach 0.9658 respectively. The best parameter number of trees is 300.