Decision Tree Over Support Vector Machine for Better Accuracy in Identifying the Problem Based on the Iris Flower

Abstract

The iris dataset will be classified using the support vector machine and decision tree algorithms. flower dataset identifies the pattern and classifies it. The dataset has 150 rows and 5 attributes, which contains 50 samples from each species. There are three species in this dataset. Iris flower classification can be performed using support vector machines and decision tree algorithms. SVM stands for Support Vector Machine, and is a supervised machine learning technique that can be used for classification and regression. The Decision Tree algorithm is a simple approach mainly used for classification and prediction. The sample size has been determined to be 20 for both the groups using G Power 80%. The Support Vector Machine algorithm provides a mean accuracy of 98.09% when compared to the Decision Tree algorithm, with a mean accuracy of 95.55%. A statistically insignificant difference was observed between the Decision Tree and the Support Vector Machine, p = 0.92 (> 0.05) based on 2-tailed analysis. In the classification of Iris flowers, the Support Vector Machine outperformed the Decision Tree Algorithm.