Comprehensive Classification of Iris Flower Species: A Machine Learning Approach

Abstract

This study employs robust machine learning techniques to comprehensively assess the classification of Iris flower species. This study investigates the effectiveness of several machine learning algorithms in reliably classifying Iris flower species by utilizing a dataset that includes crucial morphological attributes such as sepal length, sepal width, petal length, and petal width. The algorithms under consideration are Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost. Every algorithm has its own distinct methodology for classification, where Decision Trees offer clear interpretability and Random Forest and XGBoost offer strong and complex ensemble techniques. The primary aim of this study is to assess and contrast different algorithms, considering not only their classification accuracy but also significant performance metrics including precision, recall, F1-score, ROC AUC, and specificity. This research provides valuable insights into the capabilities and constraints of each methodology when implemented on a meticulously organized and defined botanical dataset. It is expected that the results of this study will contribute significantly to the fields of artificial intelligence and botanical taxonomy, highlighting the capacity of these methods to accurately identify and categorize plant species.