Iris classification is a common machine learning problem that involves categorizing iris flowers into different species or classes based on the characteristics of their petals and sepals. The dataset used for iris classification typically consists of measurements of the length and width of the sepals and petals of several iris flowers, along with the corresponding species label.
The goal of iris classification is to build a model that can accurately predict the species of iris flower based on its measurements. This problem is often used as a simple demonstration of classification algorithms in machine learning due to its straightforward nature and the availability of a well-known dataset, called the Iris dataset.
The Iris dataset consists of 150 samples of iris flowers, each belonging to one of three species: Setosa, Versicolor, and Virginica. Each sample has four features: sepal length, sepal width, petal length, and petal width, all measured in centimeters.