Mushroom Classification between Poisonous and Edible Mushrooms Using Machine Learning Techniques
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Abstract
Traditional methods of identifying poisonous mushrooms can be both inaccurate and time consuming. The most common approach involves visually inspecting the mushroom's physical characteristics, which can be labor-intensive and challenging. This study investigated the use of machine learning techniques to classify mushrooms into two categories: edible and poisonous. A public dataset of 8,124 gilled mushrooms from 23 species within the Agaricus and Leiota genera was used to train five machine learning models: logistic regression, support vector machines (SVMs), decision trees, random forests, and XGBoost. Feature selection and extraction technique were applied to identify the most important attributes for classifying mushroom species. The results indicated that
the decision tree model, coupled with recursive feature elimination (RFE), achieved the best performance when using 60% of the data and focusing on three features: odor, gill size, and spore print color. This model produced an F1 score of 99.32%. Based on these finding, a prototype web application was developed for potential future use.
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References
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