Automatická klasifikace hub metodami strojového učení

Abstract

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.