Machine Learning Algorithms Used for Iris Flower Classification

Abstract

Classification is a supervised machine learning technique which is used to predict group membership for data instances. For simplification of classification, one may use scikit-learn tool kit. This chapter mainly focuses on the classification of Iris dataset using scikit-learn. It concerns the recognition of Iris flower species (setosa, versicolor, and verginica) on the basis of the measurements of length and width of sepal and petal of the flower. One can generate classification models by using various machine learning algorithms through training the iris flower dataset, and can choose the model with highest accuracy to predict the species of iris flower more precisely. Classification of Iris dataset would be detecting patterns from examining sepal and petal size of the Iris flower and how the prediction was made from analyzing the pattern to form the class of Iris flower. By using this pattern and classification, in future upcoming years the unseen data can be predicted more precisely. The goal here is to gain insights to model the probabilities of class membership, conditioned on the flower features. The proposed chapter mainly focuses on how one can train their model with data using machine learning algorithms to predict the species of Iris flower by input of the unseen data using what it has learnt from the trained data.