Data set

An overview of the Iris dataset used in statistical classification and machine learning.

Iris dataset

Scatterplot of the Iris dataset showing the relationship between different features.

The Iris dataset is a classic and widely used dataset in the field of statistics and machine learning. It is often used as a beginner's dataset for demonstrating various classification algorithms and techniques. The dataset contains 150 samples of iris flowers, each described by four features: sepal length, sepal width, petal length, and petal width.

History

The Iris dataset was introduced by the British biologist and statistician Ronald A. Fisher in 1936. It was part of his work on discriminant analysis, a statistical technique used to distinguish between different sets of data. Fisher's work laid the foundation for many modern statistical methods and machine learning algorithms.

Structure

The dataset consists of 150 samples from three species of iris flowers: Iris setosa, Iris versicolor, and Iris virginica. Each species is represented by 50 samples. The four features measured for each sample are:

• Sepal length in centimeters
• Sepal width in centimeters
• Petal length in centimeters
• Petal width in centimeters

These features are used to classify the samples into one of the three species.

Applications

The Iris dataset is commonly used in machine learning for:

• Supervised learning: It is used to train and test classification algorithms such as k-nearest neighbors, support vector machines, and decision trees.
• Data visualization: The dataset is often used to demonstrate techniques such as scatter plots, histograms, and box plots.
• Dimensionality reduction: Techniques like principal component analysis (PCA) are applied to the dataset to reduce the number of features while preserving the variance.

Challenges

While the Iris dataset is a useful tool for learning and experimentation, it has limitations:

• It is a small dataset, which may not be representative of real-world data.
• The classes are linearly separable, which may not be the case in more complex datasets.

Related pages

• Machine learning
• Statistical classification
• Ronald A. Fisher
• Discriminant analysis