Multiclass Classification with Iris Dataset using Gaussian Naive Bayes

A prominent subset of artificial intelligence is machine learning which in today's modern era, is all around us. A model is created in machine learning based on training data and it is predicted that whether the inferences made were correct .Thus the essence of machine learning lies in data extraction and then predictions. It assists a computer to be programmed by self-learning and thereby improve its performance at a specific task. Supervised machine learning tasks primarily include classification for which various algorithms have been applied so far. In this paper, we apply a supervised learning algorithm such as Gaussian Naïve Bayes to classify the species of an Iris flower based on the length and width of their sepals and petals. The performance of the classifier is then tested in terms of its accuracy and classification metrics.