Performance Evaluation of Machine Learning Algorithms for Iris Dataset Classification

Abstract

The problem of classifying iris flowers is a well-known issue in the fields of machine learning and pattern recognition. The Iris dataset is widely recognized as a standard for evaluating the effectiveness of classification algorithms in machine learning. This paper offers a comparative assessment of different machine learning algorithms when applied to the Iris dataset. We implement and assess the performance of k-Nearest Neighbors, Logistic Regression, Decision Tree, Linear SVC methods, Random Forest, Gaussian Naïve Bayes and AdaBoost algorithms based on their accuracy in classification. We examine diverse classification methods, appraise their effectiveness, and discuss our findings. The outcomes reveal the accuracy of these models in correctly categorizing the Iris species. To achieve high accuracy, k-Nearest Neighbors, Logistic Regression, Linear SVC methods,Decision Tree,, Random Forest and AdaBoost classifiers are utilised.