Optimizing mushroom classification through machine learning and hyperparameter tuning
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Abstract
This research explores the application of machine learning in the classification of mushrooms as poisonous or edible, emphasizing the importance of optimal model performance to ensure food safety. This study compares four classification algorithms-Random Forest, Logistic Regression, Decision Tree, and Naive Bayes-before optimizing the two best models through Hyperparameter Tuning using Grid Search. The proposed method involves Exploratory Data Analysis (EDA), Data Preprocessing, Classification Modeling, Performance Evaluation, and Hyperparameter Tuning. The dataset used is Mushroom Classification data, and the results show that the Random Forest algorithm performs better with ROC values close to 100%, high recall, and good F1-Score. Hyperparameter tuning further improved the ROC and recall of the Random Forest model, emphasizing its adaptability to the nature of the dataset. This research emphasizes the importance of robust data processing and model optimization to achieve accurate and reliable predictions in mushroom classification, contributing to food safety endeavors.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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