Automatická klasifikace hub metodami strojového učení
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Vysoká škola báňská – Technická univerzita Ostrava
Abstract
Tato diplomová práce se zaměřuje na analýzu a klasifikaci hub pomocí strojového učení, přičemž hlavním cílem bylo vyhodnotit výkon různých klasifikačních algoritmů na základě veřejně dostupné datové sady hub. V práci byly implementovány a optimalizovány modely jako Random Forest, Support Vector Machine (SVM), Decision Tree (DT), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), k-Nearest Neighbors (KNN). Pro každý algoritmus byl proveden Grid Search pro ladění hyperparametrů a následně byly porovnány výsledky jednotlivých konfigurací modelů.
This thesis focuses on the analysis and classification of mushrooms using machine learning, with the main goal of evaluating the performance of various classification algorithms based on a publicly available mushroom dataset. The study implemented and optimized models such as Random Forest, Support Vector Machine (SVM), Decision Tree (DT), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), and k-Nearest Neighbors (KNN). For each algorithm, Grid Search was performed for hyperparameter tuning, and the results were subsequently compared based on accuracy and other evaluation metrics.
This thesis focuses on the analysis and classification of mushrooms using machine learning, with the main goal of evaluating the performance of various classification algorithms based on a publicly available mushroom dataset. The study implemented and optimized models such as Random Forest, Support Vector Machine (SVM), Decision Tree (DT), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), and k-Nearest Neighbors (KNN). For each algorithm, Grid Search was performed for hyperparameter tuning, and the results were subsequently compared based on accuracy and other evaluation metrics.