Flower Classification: A Comparative Study of ML Algorithms in Orange

This paper compares different machine learning models in Orange data mining tool to classify seven types of flowers using a dataset of about 140 images The study examines the performance of algorithms such as logistic regression, SVM, kNN, random forest, decision trees etc. based on key metrics such as accuracy, precision, recall, F1-score. The results indicate that Logistic Regression performed perfectly well 99.5%, followed by SVM with an accuracy of 98.9%. Random forest and kNN achieved lower accuracies of 89.9% and 78.7%, respectively. Decision trees had the lowest performance, with an accuracy of 54.7%. Logistic regression outperformed the other models in terms of accuracy and recall, thus becoming the most effective model for flower classification in this study. The results provide insights in choosing the best algorithms for this classification task.