Mushroom Classification using Random Forest and REP Tree Classifiers

Abstract

Mushroom is a popular fruit of a much larger fungus that has a high level of protein and a rich source of vitamin B. It aids in the prevention of cancer, weight loss, and immune system enhancement. There are numerous thousands of mushroom species within the world and a few are eatable and a few are noxious due to noteworthy poisons on them. Hence, it is a vital errand to distinguish between eatable and harmful mushrooms. This paper focuses on comparing the performance of Random Forest and Reduced Error Pruning (REP) Tree classification algorithms for the classification of edible and poisonous mushrooms. In this paper, mushroom dataset from UCI machine learning repository has been classified using Random Forest and REP Tree classifiers. The result based on accuracy, precision, recall and F-measure showed that the Random Forest outperformed REP Tree algorithm as it had highest accuracy value of 100%, precision value of 100%, recall value of 100% and F- measure value of 100%. The performance is 100% by using Random Forest, which is found better with respect to REP Tree classifier.