Data set

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


Iris dataset[edit | edit source]

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[edit | edit source]

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[edit | edit source]

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[edit | edit source]

The Iris dataset is commonly used in machine learning for:

Challenges[edit | edit source]

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[edit | edit source]

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