Evaluating Supervised Machine Learning Algorithms for Mushroom Classification

The classification of mushrooms as edible or poisonous is a critical task that can aid in ensuring public health and safety. With the growing capabilities of machine learning, this study investigates the effectiveness of various supervised learning algorithms in accurately classifying mushroom species based on their physical attributes. The dataset used includes several categorical features describing mushroom characteristics. Multiple algorithms, including Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR), were implemented and evaluated using performance metrics such as accuracy, precision, recall, and F1-score. The results reveal that ensemble-based methods, particularly Random Forest, offer superior classification performance compared to other techniques. This study highlights the potential of supervised machine learning as a reliable tool for biological classification tasks and provides insights into algorithm selection for similar applications