metadata

annotations_creators:
  - expert-generated
language_creators:
  - found
language:
  - en
license:
  - cc0-1.0
multilinguality:
  - monolingual
pretty_name: Iris
size_categories:
  - n<1K
source_datasets:
  - original
task_categories:
  - tabular-classification
task_ids:
  - multi-class-classification

Dataset Card for "iris"

Dataset Description

Dataset Name: Iris
Dataset Type: Tabular
Source: UCI Machine Learning Repository
Languages: English (feature labels)
License: Public Domain (freely usable)

Dataset Summary

The Iris dataset is one of the most classic datasets in machine learning, often used for classification and clustering tasks. It contains 150 samples of iris flowers, each described by four features: sepal length, sepal width, petal length, and petal width. The task is to classify the samples into one of three species: Iris setosa, Iris versicolor, or Iris virginica.

This dataset is especially useful for:

Supervised learning (classification)
Unsupervised learning (clustering)
Model explainability techniques
Feature selection and dimensionality reduction

Supported Tasks and Leaderboards

Classification: Predict the species of iris based on the four numerical features.
Clustering: Unsupervised grouping of samples into natural clusters.

Languages

The feature and label names are in English.

Dataset Structure

Data Fields

Feature	Type	Description
sepal_length	float32	Sepal length in centimeters
sepal_width	float32	Sepal width in centimeters
petal_length	float32	Petal length in centimeters
petal_width	float32	Petal width in centimeters
label	class label (str)	Species of the flower (`setosa`, `versicolor`, `virginica`)

Data Splits

There are no predefined splits, but you can randomly split the dataset for training and evaluation (e.g., 80/20 or 70/30).

Example Row

{
  "sepal_length": 5.1,
  "sepal_width": 3.5,
  "petal_length": 1.4,
  "petal_width": 0.2,
  "label": "setosa"
}