irisdata

Irisdata is a term used in education and data science to refer to datasets derived from iris flower measurements. It is not a single official dataset but a label applied to collections that share a common structure used for teaching and benchmarking machine learning algorithms.

Core structure: Most irisdata records contain four numeric features representing measurements of iris flowers: sepal length, sepal width, petal length, and petal width, typically in centimeters. Each record is associated with a species label, usually one of three classes: Iris setosa, Iris versicolor, and Iris virginica. The canonical iris dataset, often used as a reference implementation, contains 150 samples with 50 examples per class.

Usage: Irisdata is widely used for introductory classification tasks, visualization, and algorithm evaluation. Common algorithms tested include logistic regression, k-nearest neighbors, decision trees, and support vector machines. It also serves as a simple example in tutorials and textbooks.

Origins and availability: The canonical Iris dataset was introduced by Ronald A. Fisher in 1936 as a demonstration of discriminant analysis. Today, irisdata variants are distributed in many data science libraries and repositories, notably in the UCI Machine Learning Repository and in scikit-learn’s datasets module. The data is generally provided under permissive licenses suitable for education and research.

Variants and extensions: Some irisdata collections include standardized or normalized features, additional derived attributes, or metadata. Image-based iris datasets exist separately and are used for computer vision tasks rather than tabular classification.